What You Will Learn

• Distinguish foundation models from LLMs
• Compare open-source and proprietary model trade-offs
• Choose deployment strategies (cloud, on-prem, model as a service)
• Apply prompt engineering, RAG, and fine-tuning
• Use Azure AI Studio for model discovery, benchmarking, and deployment
• Adopt responsible AI and security best practices

Study Guide

Introduction: Why Comparing LLMs Matters in Generative AI

Artificial intelligence is moving from buzzword to reality, and at its heart are Large Language Models (LLMs). But not all LLMs are created equal. If you’re looking to leverage Generative AI in your business or career, understanding the differences between LLMs, how they’re built, how they’re deployed, and how to maximize their value is crucial.
This course is your roadmap. We'll break down what makes a foundation model, how LLMs fit into the AI ecosystem, why open source vs. proprietary models is a strategic decision, and the nuts and bolts of deployment strategies, including a deep dive into platforms like Azure AI Studio. You’ll get practical guidance, real examples, and the decision frameworks you need to confidently explore, compare, and implement LLMs, whether you’re a curious beginner or a business leader ready to scale AI solutions.

Foundation Models vs. LLMs: Getting the Basics Right

Before diving into the differences between LLMs, open source, and deployment strategies, you need clarity on the foundational terms.
Let’s cut through the jargon:

What is a Foundation Model?
A foundation model is a large, pre-trained artificial intelligence model designed to serve as the basis for a wide array of downstream tasks. Imagine it as a generalist: it’s read vast amounts of data (text, images, videos), learned broad patterns, and is adaptable enough to be fine-tuned or prompted for specific jobs. Key characteristics:

• Pre-trained: Already learned from massive datasets before you use it.
• Generalized: Can handle diverse tasks (not just one thing).
• Adaptable: Can be tweaked or guided for new use cases.
• Large: Contains millions or billions of parameters (the “memory” of the model).
• Self-supervised: Learns patterns and structure from data itself, not just explicit labels.

Example: CLIP by OpenAI is a foundation model that can understand both images and text.

What is a Large Language Model (LLM)?
An LLM is a specific type of foundation model built to understand, generate, and process human language. In practice, LLMs like ChatGPT, Falcon, or LLaMA are trained on giant text corpora using tokenizers (which break sentences down into manageable pieces). Every LLM is a foundation model, but it's focused on language.

• Uses language as its main data type
• Processes input via tokenizers
• Outputs human-like text, code, or even instructions

Example: ChatGPT is an LLM trained on internet text to answer questions, write articles, or summarize information.

Not All Foundation Models Are LLMs
Here’s where nuance matters. Some foundation models are multi-modal. They can process not just text, but also images, videos, or audio. These might not use language as their main data type, or might not use a tokenizer at all.

• Example: A model trained to generate images from prompts (like DALL-E) is a foundation model, but not an LLM.
• Example: A video understanding model, trained on surveillance footage or movies, can learn to predict actions without ever dealing with text,again, a foundation model but not an LLM.

In short: All LLMs are foundation models, but not every foundation model is an LLM.

Classifying Language Models: Open Source vs. Proprietary

Choosing the right LLM isn’t just about performance,it’s about control, transparency, support, and your ability to customize. The two big camps are open source and proprietary models. Each comes with unique upsides and trade-offs.

Open Source Language Models
Open source LLMs are models whose code, weights, or full architecture are made publicly available. This means you can inspect, tweak, and even retrain them for your own needs.

• Transparency: See exactly how the model works, what data it was trained on, and even how it processes prompts.
• Flexibility: Download the full model and run it on your own infrastructure. Fine-tune it for unique use cases.
• Community Driven: Improvements, bug fixes, and new features often come from a vibrant community.

Examples:

• Falcon: Released by the Technology Innovation Institute, Falcon is a fast, open source LLM with competitive performance.
• LLaMA: Developed by Meta, LLaMA’s code and weights are available for research, and it’s been widely adopted for customization.
• Mistral: Another open source LLM with a focus on efficiency and modularity.

Drawbacks:

• Support: Open source projects may lack dedicated support or timely security updates (e.g., new protection against prompt injection attacks).
• Licensing: Some open source models come with restrictive licenses, limiting commercial or large-scale use.

Example: If you’re a startup wanting to build a highly customized chatbot, an open source LLM like LLaMA lets you adapt and self-host the model. But you’ll need to manage updates and security on your own.

Proprietary Language Models
Proprietary LLMs are owned and managed by a company. Typically, you access them as a cloud service via APIs. The model itself is a “black box”,you use it, but you can’t see or alter its inner workings.

• Ease of Use: Ready-made, with simple API access. You don’t have to worry about infrastructure or model updates.
• Security & Reliability: Managed by teams with resources for continuous improvement and robust security.
• Constant Updates: New features, improved accuracy, and bug fixes are pushed automatically.

Examples:

• ChatGPT (OpenAI): Accessed through the OpenAI API or integrated into platforms like Microsoft Copilot.
• Google Gemini (formerly Bard): Available through Google Cloud APIs.
• Anthropic Claude: Accessed via enterprise-focused APIs.

Drawbacks:

• Limited Customization: You can’t change the core model, only prompt it or use predefined tuning features.
• Data Privacy: Your data is sent to an external provider, which may raise compliance or privacy concerns.

Example: A law firm looking for a secure, reliable virtual assistant may prefer ChatGPT’s API for legal research, benefiting from robust support and compliance, but sacrificing control over the underlying model.

Categories of Language Models by Function: Embeddings, Image Generation, and Text Generation

Language models aren’t just about chatbots. They underpin a wide variety of AI-powered applications, each serving distinct needs. Understanding these categories helps you match the right tool to your task.

1. Language Models for Embeddings
Embeddings are numerical representations of text (or other data types) in a multi-dimensional space, capturing semantic meaning and relationships.

• Purpose: Convert text, documents, or code snippets into vectors so that different models can “understand” and compare them.
• Applications: Powers search engines, recommendation systems, and Retrieval Augmented Generation (RAG) pipelines.

Example: When you search for “quarterly results” in your company’s knowledge base, an embedding model converts your query and documents into vectors, then finds the most relevant matches.
Example: Embeddings allow translation tools to match semantically similar phrases across languages, even if the words are different.

2. Language Models for Image Generation
These models take a text prompt and generate a visual image reflecting the description.

• Purpose: Turn your words into visuals,whether for design, storytelling, or creative brainstorming.
• Applications: Marketing, prototyping, game development, content creation.

Example: DALL-E generates custom illustrations from prompts like “a two-story house shaped like a shoe, in watercolor style.”
Example: Microsoft Copilot can create images for PowerPoint slides based on a single sentence.

3. Language Models for Text Generation
This is the classic “chatbot” use case, but it extends far beyond conversation.

• Purpose: Generate human-like text, code, summaries, or even poetry from a prompt.
• Applications: Customer support bots, document summarization, code completion, creative writing, and report generation.

Example: ChatGPT answers questions, drafts emails, or writes blog posts from a few words of instruction.
Example: GitHub Copilot (powered by LLMs) autocompletes code for software developers.

Deployment Strategies: Service vs. Model (Cloud vs. Local)

How you deploy an LLM is as critical as which one you choose. The method impacts your control, cost, security, and ability to scale. There are two main approaches: using a cloud “service” or deploying the model yourself (“model deployment”).

Service Deployment (Cloud-Based)
The LLM is hosted by a cloud provider (such as Azure, AWS, or OpenAI). You interact with it via API calls, sending prompts and receiving responses. You never touch the underlying infrastructure or raw model.

• Pros: No need to manage hardware, networking, or scaling. Security and compliance are handled by the provider. Easy integration with other cloud services.
• Cons: Less control over the model’s behavior or customization. Your data leaves your environment (potential privacy concerns).

Example: A retail company integrates ChatGPT into its website chat widget using the OpenAI API, with zero infrastructure setup.
Example: An HR team uses Copilot in Microsoft 365 to auto-generate job descriptions, all managed in the cloud.

Model Deployment (Local/On-Premises)
You download the model and set it up on your own servers or infrastructure. You control the environment, scaling, and security.

• Pros: Full control and ability to customize. Data remains on your premises, boosting privacy and compliance.
• Cons: You’re responsible for infrastructure costs, updates, scaling, and security. Requires technical expertise.

Example: A bank downloads LLaMA and deploys it within its own secure data center, fine-tuning it on sensitive financial data.
Example: A healthcare startup runs an open source LLM on a private cluster to ensure all patient data is kept in-house.

Azure AI Studio: A One-Stop Platform for Foundation Model Management

Managing LLMs isn’t just about picking a model. You need tools to discover, evaluate, fine-tune, and deploy them. Azure AI Studio brings all these capabilities into a single, user-friendly platform.

Model Catalog
A curated, searchable inventory of foundation models from multiple providers (Azure OpenAI, Meta, Hugging Face, etc.). You can filter by:

• Provider: Choose models from leading organizations
• Task: Narrow down to Q&A, summarization, object detection, etc.
• License: Find models you can use commercially or for research
• Model Name: Go straight to Falcon, LLaMA, or others

Example: You’re building a summarization tool and filter the catalog for models optimized for that task, comparing options from OpenAI and Hugging Face.
Example: You search for models with a permissive license to ensure compliance for your SaaS product.

Model Card
Each model in the catalog has a detailed “model card,” showing:

• Description: What is this model for? What are its strengths?
• Training Data: What data was it trained on?
• Use Cases: Where does it shine?
• Code Samples: How do you implement or call it?

Example: Reviewing the LLaMA 2 model card, you see sample code for integrating it into a Python app and learn about its performance on question answering.
Example: The DALL-E model card details its limitations for photorealistic image creation, helping you set user expectations.

Fine-tuning
Azure AI Studio allows you to fine-tune select models (like LLaMA 2 70B) with your own data. This process adjusts the model’s weights and biases, making it better suited to your unique use case.

• Supply custom training data (e.g., product manuals, customer chat logs)
• Generate a specialized model for your specific tasks

Example: A telecom company fine-tunes LLaMA 2 on its customer service transcripts, improving accuracy on telecom-specific terminology.
Example: A legal team fine-tunes a model on historical case data to better draft legal summaries.

Deployment Options

• Standard Deployment (Real-time Endpoint): The model is deployed in your Azure subscription, and you manage the underlying infrastructure for inference.
• Pay-as-you-go Deployment (Model as a Service): Use the model via REST APIs, without worrying about infrastructure,just like Azure OpenAI Service.

Example: A marketing agency deploys a fine-tuned LLM as a real-time endpoint for generating campaign copy, controlling resource allocation as needed.
Example: A startup uses pay-as-you-go endpoints to access LLaMA 2 for occasional code generation tasks, paying only for what they use.

Model Benchmark
Compare different models using predefined metrics (accuracy, fluency, coherence) on various test datasets. This helps you make data-driven decisions.

• Choose a subset of models and see how they perform side-by-side
• Match models to your business priorities (e.g., fluency for customer-facing apps, accuracy for data analysis)

Example: A news organization benchmarks three summarization models to find the one that produces the most readable headlines.
Example: An enterprise tests question-answering models on internal FAQs to pick the most reliable for employee support.

Approaches to Deploying LLMs into Production: Four Key Strategies

How do you make an LLM “work for you”? There’s no single answer,there are several proven strategies, and the best approach depends on your needs, resources, and goals. Let’s break down each one.

1. Prompt Engineering with Context (Zero-shot, One-shot, Few-shot Learning)
Prompt engineering is the art of crafting the input you give to an LLM to get the best output. The more context and examples you provide, the better the results.

• Zero-shot: Provide just the prompt, no examples. The model relies on its pre-training.
• One-shot: Include a single example in your prompt to guide the model.
• Few-shot: Add multiple examples, giving the model a clearer idea of what you want.
• Conversational context: Describe the assistant’s personality, style, or pass conversation history for more tailored outputs.

Example: Zero-shot , “Summarize this article in one sentence.”
Example: Few-shot , “Summarize this article. For reference: Article 1: ‘...’ Summary: ‘...’ Article 2: ‘...’ Summary: ‘...’ Now, Article 3: ‘...’ Summary:”
Tips: Be explicit. The more structure and context, the better the model’s understanding. Experiment with prompt phrasing for optimal results.

2. Retrieval Augmented Generation (RAG)
RAG is a game-changer when your LLM lacks information about recent events or private data. It works by searching external sources for relevant data, then adding those chunks to the prompt as context.

• Uses a search pipeline (like Azure AI Search with a vector database) to find the most relevant information
• Merges this external data with your prompt before sending it to the LLM

Example: A customer support bot retrieves the latest troubleshooting steps from internal documentation and includes them in the prompt to answer user queries.
Example: A financial analyst tool uses RAG to pull up-to-date market reports, supplementing the LLM’s knowledge base for real-time recommendations.
Tips: Keep your external knowledge base fresh. The quality of RAG depends on the relevance and accuracy of your external data.

3. Fine-tuning
Fine-tuning goes beyond prompt engineering,here, you retrain the model on your specific data, updating its internal weights and biases.

• Great for strict latency requirements (avoiding huge prompts)
• Delivers highly specialized models, adapted to your language, terminology, or style
• Requires high-quality, labeled data and more computational resources

Example: An insurance firm fine-tunes an LLM on claims data to automate processing with higher accuracy.
Example: An e-commerce company fine-tunes a model on product catalog data to improve search and recommendations.
Tips: Invest in data quality. Poorly labeled or inconsistent data will degrade model performance, not improve it.

4. Training Your Own LLM from Scratch
This is the “moonshot” approach,building an LLM entirely from your own data, for a truly unique use case.

• Requires massive volumes of high-quality, domain-specific data
• Needs deep expertise in machine learning, infrastructure, and data engineering
• Best for highly specialized, proprietary domains where no off-the-shelf model suffices

Example: A pharmaceutical company with decades of proprietary research data builds an LLM to automate drug discovery literature review.
Example: A government agency creates an LLM for classified intelligence analysis, using only internal, secure data.
Tips: Consider this only if you have unmatched data resources and a use case that cannot be addressed through existing models or fine-tuning.

Complementary Approaches and Responsible AI

These techniques aren’t mutually exclusive. The best solutions often combine them for maximum impact.

• Combine prompt engineering with RAG for a chatbot that both understands your tone and pulls in the latest company knowledge.
• Fine-tune a model for your domain, then use RAG to keep it updated with real-time information.
• Start with prompt engineering to validate your use case, then graduate to fine-tuning as your needs evolve.

Example: A healthcare provider fine-tunes an LLM on medical transcripts, then uses RAG to retrieve patient-specific history for each consultation.
Example: An enterprise uses prompt engineering for internal Q&A, and later adds fine-tuning and RAG as their dataset grows and requirements mature.

No One-Size-Fits-All
Every organization’s needs are different. The right mix depends on your:

• Resources: Data, compute, and skilled people
• Requirements: Security, latency, compliance
• Goals: Customization, accuracy, cost-efficiency

Responsible AI
Regardless of your chosen strategy, it’s essential to be conscious and transparent about technology limitations. Use responsible practices:

• Disclose where the AI may be uncertain or limited
• Monitor for harmful content or bias
• Keep humans in the loop for critical decisions

Example: Regularly audit your LLM's outputs for accuracy and fairness, especially in sensitive domains like healthcare or finance.
Example: Use model cards and benchmarks to communicate a model’s intended use, limitations, and performance.

Putting It All Together: Practical Decision-Making for LLMs

Let’s walk through how you might apply these concepts in real scenarios.

Scenario 1: Launching an AI-Powered Helpdesk for a Startup

• Start with prompt engineering using a proprietary API like ChatGPT for fast prototyping.
• Add RAG by connecting your FAQ documents (via Azure AI Search) to provide up-to-date answers.
• If volume grows, consider fine-tuning an open source model like LLaMA on your actual support conversations to improve response quality.

Scenario 2: Enterprise Knowledge Management System

• Choose an open source LLM for full control and on-premises deployment (data privacy is critical).
• Leverage embeddings for semantic search across internal documentation.
• Fine-tune the model on your company’s project reports and internal communications.
• Implement RAG to fetch the most relevant documents for every query.

Scenario 3: Creative Content Generation for Marketing

• Experiment with DALL-E or Copilot for image generation from campaign briefs.
• Use ChatGPT for drafting ad copy, and benchmark multiple models in Azure AI Studio to select the best for your brand’s tone.
• Deploy via cloud service for scalability and ease of integration.

Best Practices for Exploring and Comparing LLMs

• Start simple. Use prompt engineering and cloud services to validate your use case before investing in fine-tuning or custom models.
• Benchmark before you build. Use tools like Azure AI Studio’s model benchmark to compare models on your specific tasks.
• Invest in quality data. Whether fine-tuning or training from scratch, the quality of your data determines the quality of your results.
• Stay updated. Both open source and proprietary models evolve rapidly. Monitor releases, security updates, and community feedback.
• Prioritize responsible AI. Always consider the ethical and compliance implications of deploying LLMs at scale.

Conclusion: Turning AI Understanding Into Action

You now have the blueprint for navigating the world of Large Language Models. You know how to distinguish foundation models from LLMs, weigh the pros and cons of open source versus proprietary models, and choose the right deployment strategy for your needs. You understand the categories of language models, how embedding powers modern AI applications, and how platforms like Azure AI Studio can streamline your LLM journey.
The next step is to apply this knowledge: start experimenting, benchmark different models for your use case, and combine strategies like prompt engineering, RAG, and fine-tuning for maximum impact. Above all, remember that there is no universal solution,your path depends on your unique business requirements, resources, and a commitment to responsible AI practices.
Generative AI is a toolkit. With the right understanding and approach, you’re ready to unlock its full potential for your organization or career.

Frequently Asked Questions

This FAQ is designed to provide practical, clear answers to the most common and essential questions about exploring and comparing different Large Language Models (LLMs) for beginners. Whether you’re just starting or already experimenting with generative AI in your business, these questions cover fundamental concepts, technical distinctions, real-world use cases, and best practices for deploying and customising LLMs. You’ll find both foundational explanations and actionable advice for choosing, evaluating, and implementing LLMs in a business setting.

What is the distinction between a Foundation Model and a Large Language Model (LLM)?

Foundation Models are the base for constructing new AI solutions, characterised by being pre-trained, generalised, adaptable, large, and self-supervised. They can handle various data types (multimodal) and perform multiple tasks, though not always perfectly without guidance.
LLMs are a type of Foundation Model because they fulfil all these five characteristics; however, not all Foundation Models are LLMs, as some do not primarily use language and may employ different learning mechanisms, such as multimodal data without relying on tokenisers.

What are the main differences between open-source and proprietary Language Models?

Open-source language models provide access to some or all of their components, such as training code, weights for fine-tuning, or even the full model for direct download and modification. They offer flexibility and community-driven development but may lack consistent support, updates, and security enhancements due to limited resources from research groups or non-profit organisations.
Proprietary models, typically offered as cloud services via APIs, are often more user-friendly, consistently updated, and have robust security. While they offer less direct control and fine-tuning opportunities, they provide a reliable, ready-made solution with built-in scalability and security.

How are language models typically categorised by their function?

Language models are commonly categorised into three main functions:

• Embedding Models: These models convert text strings into numerical representations called embeddings, facilitating communication between different language systems and enabling efficient data storage and retrieval in systems like RAG (Retrieval Augmented Generation).
• Image Generation Models: As the name suggests, these models take a text prompt and generate an image based on the input, exemplified by tools like DALL-E and Copilot.
• Text Generation Models: These are widely known for generating human-like text or code from a given prompt, with popular examples including ChatGPT, Falcon, and LLaMA, often accessible through platforms like Hugging Face.

What is the difference between interacting with an AI "service" versus an AI "model"?

Interacting with an AI service means utilising models hosted in the cloud, typically via API calls. This approach offloads infrastructure management, scalability, and security to the cloud provider, making it easier to integrate with existing services and manipulate outputs, often through fine-tuning.
Conversely, interacting with an AI model involves downloading and setting up the model on your own infrastructure, giving you full control over the model and its pipeline. However, this also means you are responsible for managing scalability, security, and infrastructure costs, which can be significantly more complex and resource-intensive.

Why use Azure AI Studio for Foundation Models?

Azure AI Studio serves as a comprehensive platform for developing, testing, and managing the entire lifecycle of AI applications, especially with Foundation Models. It integrates Microsoft's data technologies for optimised storage, offers a wide range of proprietary and open-source LLMs (from providers like OpenAI, Meta, Hugging Face, and Mistral), and provides tools for responsible AI development, prompt engineering, evaluation, and monitoring.
It streamlines the process of experimenting with and deploying models, offering features like a model catalogue for easy discovery, model cards with detailed information, and options for fine-tuning and deployment (including "model as a service" for simplified consumption).

What are the different approaches to deploying Large Language Models into production?

There are four main approaches for deploying LLMs into production, each with varying complexities, costs, and quality levels:

• Prompt Engineering with Context: This involves feeding the pre-trained LLM detailed prompts, including examples (one-shot or few-shot learning) or conversation history, to guide its responses. It's cost-effective and a good starting point, especially for general language tasks.
• Retrieval Augmented Generation (RAG): This technique addresses LLMs' knowledge limitations by adding external, up-to-date, or private data (e.g., from vector databases like Azure AI Search) to the prompt. RAG boosts performance and reduces inaccuracies without requiring model retraining, ideal when fine-tuning is not feasible.
• Fine-tuning: This process customises an existing LLM for a specific task by updating its weights and biases using high-quality custom training data. It's suitable for strict latency requirements or when maintaining specific domain knowledge over time, though it requires additional computational resources.
• Training Your Own LLM: This is the most demanding approach, involving creating an LLM from scratch. It requires vast amounts of high-quality data, skilled professionals, and significant computational power, typically considered only for highly domain-specific use cases with abundant, domain-centric data.

Can different LLM deployment techniques be combined?

Yes, the various deployment techniques,prompt engineering, Retrieval Augmented Generation (RAG), and fine-tuning,are not mutually exclusive but rather complementary. Businesses often combine these approaches to achieve optimal results based on their specific requirements, resources, and goals. For example, one might use prompt engineering alongside fine-tuning, or even all three techniques together, to leverage their respective strengths and address different aspects of an application's needs.

What ethical considerations are important when deploying AI models?

Regardless of the training and deployment choices made, it is crucial to be conscious and transparent about the technology's limitations. Additionally, it is essential to employ responsible practices and tools throughout the AI development and deployment lifecycle.
This includes considering potential biases, ensuring fairness, maintaining privacy, and addressing security concerns like prompt injection to foster trustworthy and beneficial AI applications.

What is the difference between a Language Model, a Large Language Model (LLM), and a Foundation Model?

A Language Model is any AI model designed to process and generate human language.
A Large Language Model (LLM) is a subset of Language Models, distinguished by their large scale (billions of parameters) and foundational, general-purpose nature.
A Foundation Model is an even broader category: large, pre-trained, generalised, adaptable, self-supervised models. While all LLMs are Foundation Models, not all Foundation Models are LLMs,some work with images, audio, or multimodal data rather than language.

What role do "embeddings" play in generative AI systems?

Embeddings are numerical representations of text or other data that capture semantic meaning. They make it possible for AI systems to process, compare, and retrieve information efficiently.
For example, in Retrieval Augmented Generation (RAG), embeddings are used to search for relevant documents or data chunks to add as context to a prompt, allowing the LLM to provide more accurate and context-aware responses. Embeddings are also key for cross-model interoperability.

What are "zero-shot", "one-shot", and "few-shot" learning in prompt engineering?

These terms describe how much guidance (examples) you provide to an LLM in your prompt:

• Zero-shot learning: You provide only the question or task, with no examples. The LLM must rely entirely on its pre-existing knowledge.
• One-shot learning: You include a single example to guide the LLM on how to respond.
• Few-shot learning: You provide several examples, giving the LLM more context about the expected output or style.

This approach can significantly improve output quality, especially for specific or nuanced tasks.

Why is Retrieval Augmented Generation (RAG) valuable for businesses?

RAG is especially useful when your data needs are dynamic or proprietary. LLMs can only generate responses based on what they were trained on, which may be outdated or lack company-specific information.
RAG uses a search system to gather up-to-date or private information and provides it as context to the LLM, so answers incorporate your latest data. This reduces hallucinations and improves relevance for business-specific queries, such as customer support or internal knowledge bases.

When should you choose fine-tuning an LLM over prompt engineering with context?

Fine-tuning is preferred when you have:

• High-quality, labelled, domain-specific training data
• Strict latency requirements (where adding a lot of context to prompts would slow down responses)
• Long-term needs for highly specialised behaviour or terminology

Prompt engineering is simpler and more cost-effective for general tasks or when you lack the resources for fine-tuning.

What are the main challenges in training an LLM from scratch?

Training an LLM from the ground up is resource-heavy. It requires:

• Large volumes of high-quality, domain-specific data
• Significant computational resources (such as powerful GPUs and storage)
• Expertise in model architecture, training, and evaluation
• Long development cycles and high upfront costs

Most organisations only consider this for very niche or regulated domains where no suitable existing models are available.

How can you combine different LLM deployment techniques for optimal results?

You can layer techniques for better outcomes. For example, a company might:

• Fine-tune an LLM on their unique product data to align the model with internal terminology and tone
• Use RAG to inject the latest product updates or customer documentation into the prompt at runtime

This delivers answers that are both accurate on company knowledge and reflect up-to-date information.

How does Azure AI Studio’s Model Catalog help in selecting the right Foundation Model?

The Model Catalog in Azure AI Studio provides a searchable, filterable library of Foundation Models from various providers. Each entry includes a Model Card with details such as:

• Intended use cases
• Training data description
• Licensing information
• Sample code
• Performance benchmarks

This transparency helps users quickly compare models and choose the one that fits their business needs and compliance requirements.

What are the typical drawbacks of proprietary LLMs for businesses?

Proprietary LLMs limit transparency and customisation. Key challenges include:

• Less control over model internals and updates
• Limited opportunities for deep fine-tuning or modification
• Dependence on vendor roadmaps and pricing
• Potential data privacy concerns if sensitive data is processed in the cloud

However, they do offer ease of use, security, and regular improvements.

What are the main advantages and challenges of open-source LLMs?

Advantages:

• Full access to model code and weights for investigation and customisation
• No vendor lock-in
• Community-driven innovation
• Potential for cost savings if self-hosted

Challenges:

• Limited support and documentation
• Unclear licensing for commercial use in some cases
• Security and maintenance are your responsibility
• Requires in-house technical expertise

How do embeddings enable communication between different language models?

Embeddings translate words or sentences into vectors,numerical formats that can be shared between models. This standardisation enables:

• Compatibility between different AI systems (for example, using an embedding model from one provider and a text generation model from another)
• Efficient search and retrieval workflows (such as RAG), as different models can understand the same embedded data
• Seamless integration into pipelines where multiple models need to “speak the same language”

What does “latency” mean in the context of LLM deployment, and why does it matter?

Latency is the time it takes for a model to process an input and return a result. In business applications, high latency can slow down user experiences,think customer support chatbots or live document summarisation.
Fine-tuning a model for a specific task can reduce latency compared to passing large amounts of context in prompts, making responses faster and more predictable.

What is “Model as a Service” and when should you use it?

Model as a Service means accessing an LLM through a cloud provider’s API, without managing any infrastructure. You simply send requests and get results.
Use it when you want:

• Scalability without technical overhead
• Simple integration with cloud-based workflows
• Access to regular security and performance updates

It's ideal for organisations without dedicated AI infrastructure or those prioritising speed to deployment.

How does RAG compare to fine-tuning for handling private or up-to-date company information?

RAG is typically better for integrating frequently changing or sensitive company data, since you can dynamically retrieve and inject the latest information into prompts.
Fine-tuning is best when you have a static, high-quality dataset and need the LLM to “bake in” specific knowledge or tone over time.
Many businesses combine both, using fine-tuning for core domain expertise and RAG for live updates.

What is prompt injection, and how can you protect LLMs against it?

Prompt injection is a security risk where malicious inputs are crafted to manipulate or bypass an LLM’s intended safety guidelines.
To mitigate this:

• Sanitise and validate user inputs
• Implement strict access controls
• Monitor and audit model outputs for unexpected behaviour
• Choose models and platforms with robust security features

Are all open-source LLMs free for commercial use?

No. Open-source does not always mean unrestricted commercial use. Some models have licenses that restrict certain applications or require attribution, while others are completely free for any use. Always review the model’s license and consult your legal team before deploying in commercial settings.

What are some practical business applications of LLMs today?

LLMs can drive value in a variety of contexts:

• Automated customer support chatbots
• Internal knowledge base assistants
• Generating marketing copy or product descriptions
• Summarising legal or financial documents
• Supporting code generation and review (e.g., with Copilot)
• Image generation from text for design or prototyping

For example, an insurance company might use RAG to help agents answer customer queries using the latest policy documents.

How do you evaluate and compare different LLMs for your use case?

Use platform tools like Model Benchmark in Azure AI Studio, which let you compare models based on key metrics such as accuracy, fluency, and coherence.
You should also test models with your own sample data and typical use cases. Look for:

• Performance on your specific tasks
• Latency and scalability
• Compliance with security and privacy requirements
• Ease of integration with your existing systems

What factors should businesses consider before choosing between a cloud service and self-hosted LLM?

Key considerations include:

• Data sensitivity: Self-hosting may be preferred if handling highly confidential data
• Resource availability: Cloud services reduce infrastructure and maintenance burdens
• Customisation needs: Self-hosted solutions offer deeper control and tuning
• Budget: Cloud services can be more cost-effective for smaller workloads; self-hosting may save costs at large scale
• Compliance: Regulatory requirements may dictate where and how data is processed

What tools are needed to implement Retrieval Augmented Generation (RAG)?

A typical RAG setup involves:

• An LLM for text generation
• An embedding model to convert documents into vectors
• A vector database (like Azure AI Search) for storing and retrieving relevant context
• Pipeline logic to fetch, filter, and format retrieved content before passing it to the LLM

Why is high-quality training data so important for fine-tuning or training LLMs?

High-quality, well-labeled data ensures the LLM learns the correct patterns and produces reliable outputs. Poor data leads to poor performance, bias, or even harmful outputs.
For example, if you’re fine-tuning a model for customer support, training on clear, accurate, and representative support tickets will yield far better results than using noisy or irrelevant data.

Can Foundation Models work with data types besides text?

Yes. Unlike LLMs, which are focused on language, some Foundation Models are multimodal,they can process images, audio, video, and more. For instance, DALL-E generates images from text, and some models can combine text and image understanding for tasks like visual question answering.

Is fine-tuning available for all models in Azure AI Studio?

No, fine-tuning is available for a subset of models (for example, LLaMA 2 and some OpenAI models). The availability depends on licensing, technical compatibility, and provider support. Always check the Model Card or documentation for details.

What are some common misconceptions about LLMs?

• LLMs “understand” like humans: LLMs generate outputs based on learned patterns, not actual comprehension.
• More parameters always means better performance: Model quality depends on data, architecture, and use case fit,not just size.
• Open-source models are always best for customisation: While flexible, they require more in-house expertise and resources.

How do LLM updates work in cloud services versus self-hosted models?

In cloud services, providers handle updates, bug fixes, and performance improvements, so you benefit automatically.
With self-hosted models, you’re responsible for monitoring new releases, testing, and deploying updates to your infrastructure.

Do you need to be a programmer to use LLMs in your business?

Not necessarily. Many cloud services and platforms like Azure AI Studio offer user-friendly interfaces, API integrations, and low-code tools that allow non-technical professionals to experiment and deploy LLMs.
However, technical expertise is helpful for advanced customisation, integration, or troubleshooting.

What are the main cost drivers for deploying LLMs?

Costs can come from:

• API usage fees (for cloud services)
• Infrastructure (compute, storage, networking) if self-hosted
• Data preparation and annotation for fine-tuning
• Ongoing maintenance and monitoring

Careful workload estimation and pilot testing can help manage costs.

How can businesses address privacy concerns when using LLMs?

• Use self-hosted models or on-premise deployments for sensitive data
• Choose cloud providers with strong privacy and compliance certifications
• Anonymise or obfuscate personally identifiable information (PII) before sending data to an LLM
• Implement strict data access and retention policies

What future trends should business professionals watch in the LLM space?

Key trends include:

• Growth of multimodal Foundation Models that combine text, images, and audio
• Improvements in model efficiency and cost-effectiveness
• Increased focus on responsible AI and explainability
• More open-source options with commercial-friendly licenses
• Wider adoption of RAG and hybrid deployment strategies