What You Will Learn

• Compare LLM trade-offs: cost, latency, and capability
• Choose models by task: reasoning, long-context, and up-to-date knowledge
• Design custom evaluation metrics and RAG pipelines
• Decide between open-weight and closed models for deployment
• Apply scaling and efficiency strategies for cost-effective inference

Study Guide

Introduction

Welcome to the comprehensive guide on choosing the best Large Language Model (LLM) for your needs. In a world where AI is rapidly evolving, the ability to select the right LLM can be a game-changer for businesses, developers, and tech enthusiasts alike. This course aims to demystify the complex landscape of LLMs, providing you with the knowledge and tools to make informed decisions.
Why is this valuable?
With new models emerging almost daily, each boasting unique capabilities and pricing, navigating this landscape can be daunting. Understanding how to evaluate these models based on your specific requirements can save time, resources, and potentially transform your operations.

The Rapidly Evolving LLM Landscape

The world of LLMs is expanding at an unprecedented pace. Louie Peters, CEO of Towards AI, notes that "new models are coming out every single day and new capabilities are coming new prices." This evolution is driven by fierce competition among AI companies, leading to rapid advancements but also increasing complexity in model selection.

Practical Application:
Imagine you're a developer tasked with integrating an LLM into a customer service chatbot. The sheer number of available models can be overwhelming. However, by understanding the landscape, you can narrow down choices to those models that offer the best balance of cost and performance for your specific task.

Tip:
Stay updated with AI forums and publications to track the latest model releases and trends. This will help you remain informed about the capabilities and limitations of new models as they emerge.

Divergent Trends in LLM Development

The development of LLMs is diverging into two main trends: expensive, high-capability models and cheaper, efficient alternatives. Companies like OpenAI are releasing models like the 01 Pro, which are significantly more costly but offer enhanced reliability and capabilities. Conversely, companies like DeepSeek and Google are producing models like Gemini Flash, which are much cheaper yet still highly capable.

Example 1:
OpenAI's 01 Pro model costs ten times more than its 01 model, offering superior reasoning capabilities. This makes it ideal for tasks requiring high precision, such as legal document analysis.

Example 2:
DeepSeek's models, on the other hand, are hundreds of times cheaper to run than the original GPT-4, making them suitable for high-volume tasks like real-time social media monitoring.

Best Practice:
Evaluate the trade-off between cost and performance by considering the specific requirements of your task. For instance, if you're running a startup with limited resources, a cheaper model might be more practical, while a larger enterprise might benefit from the enhanced capabilities of a more expensive model.

Barriers to Widespread LLM Adoption

Despite their potential, several barriers hinder the widespread adoption of LLMs. Louie Peters identifies three key blockers: lack of imagination and intuition, insufficient education, and limitations in model capability and reliability.

Example 1:
A marketing team might struggle to imagine how an LLM can automate content creation, missing out on efficiency gains.

Example 2:
A company might underutilize an LLM due to a lack of understanding of how to provide the necessary context and instructions, leading to suboptimal results.

Tip:
Encourage teams to "think AI first" by integrating AI literacy into professional development programs. This will help build intuition for where and how AI can be effectively applied.

The Role of Open Source Models

Open-source models offer users more flexibility and control. While fully open-source models are rare, open-weight models like Meta's Llama allow users to download and customize model weights.

Example 1:
A developer can use Meta's Llama model weights to fine-tune the model for specific tasks, such as sentiment analysis in niche markets.

Example 2:
DeepSeek's open-weight models can be used to build custom applications, offering a cost-effective way to leverage advanced AI capabilities.

Best Practice:
When using open-weight models, ensure you have the technical expertise to handle customization and integration, as the lack of transparency in training data can pose challenges.

Scaling Laws and Alternative Improvement Strategies

Scaling laws indicate that increasing training compute leads to predictable improvements in model performance, but these gains exhibit diminishing returns. Beyond scaling, other strategies like better pre-training data and algorithmic efficiency are crucial.

Example 1:
DeepSeek's algorithmic efficiency allows them to produce high-performing models without the massive compute costs associated with traditional scaling.

Example 2:
Reasoning models that improve inference-time scaling offer significant gains with less computational investment, making them attractive for tasks like real-time decision-making.

Tip:
Consider building custom LLM pipelines on top of foundation models to enhance capability and reliability without massive capital investment.

Navigating Model Choice: Key Considerations and Recommendations

Choosing the right LLM involves several considerations, including critical functionality, context length, knowledge training data, benchmarks, arenas, custom evaluations, price, and latency.

Example 1:
If your task involves processing large volumes of text, a model like Gemini with strong long-context performance is ideal.

Example 2:
For applications requiring up-to-date knowledge, a model with a recent training data cutoff, like Gemini Pro 2.5, is preferable.

Best Practice:
Develop custom evaluation methods specific to your use case to objectively assess model performance. This will help facilitate easy switching between models if needed.

Current Favourite Models

Louie Peters shares his current favourite models, categorized by reasoning capabilities and general purpose. For reasoning tasks, models like 01 Pro and DeepSeek R1 are recommended. For non-reasoning tasks, models like GPT-4o and Gemini Flash 2.0 are favoured.

Example 1:
01 Pro is suitable for complex tasks requiring high reasoning accuracy, such as financial forecasting.

Example 2:
Gemini Flash 2.0 excels in cost-efficient inference, making it a great choice for applications like customer sentiment analysis.

Tip:
Regularly review and update your model choices as new models and updates frequently change the optimal selections.

The Future of LLM Competition and Accessibility

The competitive landscape of LLMs suggests that a monopoly is unlikely. Advancements in efficiency and alternative improvement strategies ensure that both open and closed models offer access to cutting-edge AI.

Example 1:
The availability of both open and closed models allows businesses to choose the best fit for their needs, whether prioritizing cost, performance, or flexibility.

Example 2:
The ability to customize and build on top of these models unlocks their full potential for a wide range of applications, from healthcare to finance.

Best Practice:
Leverage the competitive landscape by combining multiple models from different providers within a pipeline to achieve optimal performance.

Towards AI's Role in LLM Education

Towards AI is committed to making AI more accessible through education. They offer courses on building custom LLM pipelines and agents, covering various techniques and tools.

Example 1:
Their courses for developers include building custom LLM pipelines, which can enhance capability and reliability for specific tasks.

Example 2:
For non-developers, Towards AI provides resources to gain foundational AI and programming skills, empowering a broader audience to engage with AI technology.

Tip:
Consider enrolling in courses or workshops offered by organizations like Towards AI to stay current with AI advancements and improve your LLM utilization skills.

Conclusion

Congratulations! You've now navigated the intricate landscape of Large Language Models. By understanding the rapidly evolving LLM environment, the divergent trends in model development, and the barriers to adoption, you are equipped to make informed decisions in selecting the best LLM for your needs. Remember, the thoughtful application of these skills can transform the way you work, innovate, and compete in the AI-driven world. As you move forward, continue to explore, experiment, and educate yourself on the latest advancements to fully leverage the power of LLMs in your endeavors.

Podcast

Frequently Asked Questions

Introduction

Welcome to the FAQ section for the 'Video Course: How to Choose the Best LLM?'. This resource is designed to guide you through the complex and dynamic landscape of Large Language Models (LLMs). Whether you're a beginner just starting to explore AI or an experienced professional looking to deepen your understanding, this FAQ aims to provide clear, practical insights into selecting the most suitable LLM for your needs.

Why is it becoming increasingly difficult to choose the best Large Language Model (LLM) for a given task?

The landscape of LLMs is evolving at an unprecedented rate. New models with varying capabilities and pricing structures are released almost daily. This rapid progress, driven by intense competition, is leading to a divergence in model development. On one hand, we see the emergence of highly expensive models promising superior reliability and novel capabilities, exemplified by OpenAI's latest offerings. Conversely, breakthroughs in training methodologies are also yielding much cheaper models, like DeepSeek and Google's Gemini Flash, which often surpass the capabilities of older, more expensive models. This simultaneous trend towards both premium and budget-friendly options creates a complex decision-making environment for both non-technical users and developers.

For someone new to using LLMs, which model would you recommend as the most beginner-friendly?

While it's difficult to give a definitive answer, OpenAI's GPT-4 model, particularly after its recent upgrades, stands out as a strong all-rounder. It offers a good balance of capabilities across various tasks, including coding, instruction following, and image generation, and is accessible through the widely known and user-friendly ChatGPT interface. Additionally, Google's AI Studio provides a platform with free access to many of their latest and capable models, even for those without extensive technical expertise, making it another excellent option for experimentation.

There's a lot of discussion around open source LLMs. What is the difference between "open source" and "open weights" in the context of AI models, and are there any truly competitive fully open source LLMs?

It's crucial to distinguish between "open source" and "open weights". A truly open source AI company would provide complete transparency regarding its training data, potentially even releasing the full dataset, along with all the code and processes used to train the model, allowing others to fully recreate it. "Open weights" simply means that the company releases the pre-trained model weights for free download, enabling users to build upon them, but without the comprehensive details needed for independent recreation. Currently, there aren't many leading, highly competitive LLMs that are fully open source in the strictest sense. While companies like Meta (with its Llama models), DeepSeek, and Mistral offer many open weights models, they typically don't disclose all the specifics of their training data and processes.

How does the concept of "scaling laws" relate to the training and capabilities of LLMs, and are we seeing diminishing returns in model improvement through increased computational power?

Scaling laws in AI research describe the predictable improvements in LLM performance that occur when the amount of training compute (the number of GPUs working over time) is increased. More compute, achieved by increasing training data size or the number of model parameters, generally leads to more capable models. While these scaling laws have been instrumental in driving AI progress, they operate on a power law, meaning that as models become larger and more sophisticated, the performance gains for each subsequent increase in compute tend to diminish. This leads to rapidly escalating training costs for the most advanced models.

Beyond simply increasing computational power, what are some other key methods being used to enhance the intelligence and capabilities of LLMs?

Fortunately, relying solely on scaling training compute has diminishing returns. Several other crucial approaches are being employed to improve LLMs. These include using higher-quality and even synthetic pre-training data, developing more efficient model architectures (as demonstrated by DeepSeek), employing sophisticated post-training techniques to better align models with human instructions, leveraging "reasoning models" that allow the AI to think and plan its responses, and building custom LLM pipelines on top of foundational models to achieve targeted improvements for specific tasks.

With the emergence of more affordable yet powerful LLMs, do we still need to pursue the development of extremely large and expensive models?

Despite the increasing accessibility and capability of cheaper models, there is still a case for developing very large and expensive models. The various methods for improving AI intelligence, including scaling compute, are often additive. Combining massive computational power with better data, architectures, and reasoning capabilities is likely to yield the most advanced and versatile AI. Furthermore, these large models can unlock entirely new capabilities. However, for many practical applications, the cost of these cutting-edge models may not be justified, and more cost-effective alternatives can offer superior performance and efficiency. The key is to strategically choose the right model for the specific task and balance capability with cost.

How can users and developers effectively navigate the growing number of available LLMs and determine which ones are best suited for their needs?

The increasing variety of LLMs necessitates a strategic approach to model selection. Both users and developers should strive to build an intuition for the strengths and weaknesses of different models through experimentation and by staying informed about the latest advancements. For critical tasks, combining multiple models from different providers within a pipeline can often yield the best results. It's also essential to develop robust evaluation methods tailored to specific use cases to objectively assess model performance and facilitate easy switching between models if needed. While generic benchmarks can provide a broad comparison, real-world performance often requires custom evaluation.

Given the rapid advancements and varying strengths of different LLMs, what are some of the currently favoured models for specific use cases, considering factors like reasoning, cost-efficiency, and open source availability?

The optimal model choice depends heavily on the specific application and priorities. Currently, for complex reasoning tasks demanding high accuracy, models like O1 Pro and DeepSeek R1 are strong contenders. For cost-efficient inference, Gemini Flash 2.0 and DeepSeek V3 excel. Claude 3.7 is particularly adept at generating working code. For those seeking to fine-tune or deploy reasoning models themselves, Alibaba's QWen offers a more accessible open source option. Gemini Pro 2.5 is proving valuable for complex Retrieval-Augmented Generation (RAG) applications due to its long context window and up-to-date knowledge. General-purpose workhorses like GPT-4 (post-update) remain versatile, while GPT-4.5 and Claude 3 are chosen for complex dialogue requiring extensive knowledge and nuanced understanding. For developers wanting full control through fine-tuning, Llama and QWen's 70 billion parameter models are excellent open source choices. It's important to note that this landscape is constantly shifting, and new models and updates frequently change the optimal selections.

According to Louie Peters, what are the three main blockers preventing wider adoption of LLMs beyond basic usage?

Louie Peters identifies three main blockers as: lack of imagination and intuition on how LLMs can aid work tasks, insufficient education on how to use AI appropriately, and the fact that model capability and reliability are still lacking for many applications.

What is the core idea behind "reasoning models" and how does it typically impact the cost of using these models?

Reasoning models are designed to spend time "thinking" or planning their answer before generating the final response. This increased processing often leads to more accurate and complex answers but can significantly increase the cost of using the model due to the greater computational resources required.

Why is it important for both developers and end-users of LLMs to build an intuition for the strengths of different models?

Building an intuition for the strengths of different models allows users and developers to select the most appropriate model for each specific task, potentially leading to better results, lower costs, and improved efficiency by leveraging the unique capabilities of various LLMs.

Name two key features or capabilities of an LLM that should be considered when choosing a model for a specific task.

Two key features to consider are context length (the amount of text the model can process at once) and knowledge training date/cutoff (how up-to-date the model's information is).

According to Louie Peters, what is the primary benefit of building custom evaluation methods for LLM pipelines?

The primary benefit of building custom evaluation methods is that they are specifically tailored to the real-world problems the LLM pipeline is trying to solve, allowing for the detection of performance changes and potential errors relevant to the specific use case.

Give an example of a scenario where using a cheaper LLM model might be more suitable than using a more expensive, cutting-edge model.

A cheaper LLM model might be more suitable for high-volume, simpler automated tasks such as large-scale data extraction or as an intermediary step in a more complex LLM pipeline where speed and cost efficiency are prioritised over nuanced reasoning.

According to Louie Peters, what is one of the main focuses of Towards AI in terms of helping people engage with large language models?

Towards AI focuses on making AI more accessible by teaching people how to become advanced LLM developers who can build custom LLM pipelines and agents for various tasks, thereby unlocking greater reliability and capability from existing models.

Critically evaluate the role of benchmarks and leaderboards in assessing the capabilities of large language models. What are their limitations, and what alternative methods of evaluation are crucial for real-world applications?

Benchmarks and leaderboards provide a standardized way to compare the performance of different LLMs on specific tasks. However, they often fail to capture real-world complexities and can lead to overfitting models to specific tests rather than general capabilities. Alternatives include custom evaluation frameworks that mimic actual use cases, user feedback loops, and stress-testing models in diverse environments. These methods help ensure that models perform well beyond controlled settings and can handle the nuances of real-world applications.

Based on the information provided, outline the key factors that individuals and organisations should consider when selecting an LLM for a specific task or application. How can a strategic approach to model selection lead to more effective and efficient AI implementation?

Key factors include the model's task-specific performance, cost-efficiency, context length, and the recency of its training data. A strategic approach involves evaluating these factors against the specific needs of the task, experimenting with different models, and possibly combining multiple models for complex tasks. This approach leads to more effective AI implementation by aligning model capabilities with business objectives, optimizing resource use, and ensuring scalability.

Explore the evolving landscape of LLM competition, considering the roles of open source models, major technology companies, and emerging AI labs. What are the potential benefits and risks associated with the current state of competition in this rapidly developing field?

The LLM competition landscape is marked by a mix of open source initiatives, major tech players, and innovative AI labs. Open source models democratize access and foster innovation, while tech giants offer robust, scalable solutions. Emerging labs often drive breakthroughs in efficiency and specialization. Benefits include rapid innovation, diverse options, and competitive pricing. However, risks involve potential monopolization by a few large entities, privacy concerns, and ethical challenges in AI deployment.