AI Evolution: From Simple Data Collection to LLM Development
Data volume exploded. Big data followed. That set the stage for the AI era-chatbots, assistants, and generative systems stepping into daily workflows across engineering and product teams.
We've gone from brittle, rule-based programs to models that infer, generalize, and converse. Today, you can ship features that learn from feedback, summarize logs, and automate parts of your stack without handcrafting every rule.
From Rules and OOP to Learning Systems
Early systems were rule-first. Think OOP-heavy codebases where we hard-coded definitions and edge cases. These systems were precise, but fragile.
Example: define a cat as "two ears, four legs, fur, likes to play." A hairless cat breaks the classifier. That was normal-computers followed our rules, not patterns in data.
Deep Learning: Accuracy With a Data Appetite
The 2010s brought deep learning, a branch of machine learning that learns features directly from data. It reads text, images, audio, and video-and gets very good at recognition.
Trade-off: it needs volume and coverage. Models excel at what they've seen and drift on what they haven't. For teams, that means data pipelines, labeling quality, and evaluation loops became core engineering concerns.
LLMs: Generalization, Dialogue, and Tool Use
Large language models (LLMs) are trained on broad corpora: docs, code, forums, public sites. They predict text, but in practice they reason, plan, and interact with tools through prompts and APIs.
This is why modern assistants-ChatGPT, Gemini, Claude, Copilot-feel flexible. Ask for a cat explained as a pop song? It composes. Ask for a migration plan with code scaffolding? It drafts and iterates with you.
Where AI Already Lives in Products
- Email filters and classifiers that learn user-specific patterns.
- Recommendation engines that infer interests from behavior, not follows.
- Assistants that summarize tickets, logs, PRs, and generate tests or SQL.
Some reports cite frontier models scoring near top-percentile on a range of exams. Useful, but what matters for teams is capability-per-dollar, latency, privacy, and evals that match your actual tasks.
Implications for Engineers and Teams
- Data > parameters: invest in clean, permissioned, well-documented data. Garbage-in still applies.
- RAG first: retrieval-augmented generation often beats fine-tuning for dynamic knowledge and governance.
- Fine-tune selectively: use it for style, domain jargon, or constrained outputs when retrieval isn't enough.
- Eval like a product: define task-specific metrics (accuracy, toxicity, hallucination rate, latency, cost).
- Guardrails are features: schema-constrained outputs, content filters, and deterministic post-processing.
- Tool use matters: structured prompts, function calling, and small utilities turn LLMs into reliable agents.
30-Day Build Plan (Practical)
- Week 1: Pick one workflow with clear ROI (e.g., support summarization or log triage). Write success metrics.
- Week 2: Stand up a retrieval layer (vector DB + metadata). Add observability for prompts, costs, and errors.
- Week 3: Ship a narrow pilot to 5-20 users. Collect thumbs-up/down and error exemplars.
- Week 4: Add evals, guardrails, and fallback strategies. Optimize prompts and retrieval. Decide on fine-tuning only if gaps persist.
Model and Data Choices
- Model selection: balance capability with latency and budget. Smaller models with good RAG can win.
- Privacy: separate PII, apply row- and field-level controls, and prefer retrieval over training on sensitive data.
- Observability: log prompts, context, outputs, and user feedback. Build a feedback-to-fix loop.
Events and Momentum
At SMBC TechConnect 2025 (Jakarta, Nov 24), industry voices highlighted the shift from rigid, rule-based systems to LLMs that handle ambiguous inputs and multi-step tasks. The takeaway for builders: treat AI as a product capability-data, retrieval, evals, and guardrails-not a one-off API call.
Further Learning
The opportunity is clear: ship one narrow, valuable use case, measure it, then iterate. Tight loops beat big bets.
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