Learning Paths

Understand tokens, context limits, probability, and failure modes.

1. 1 How LLMs Actually Work: Tokens, Context, and Probability

   A production-minded explanation of what LLMs actually do under the hood—and why tokens, context windows, and probability matter for cost, latency, and reliability.

2. 2 Prompting Is Not Magic: What Really Changes the Output

   Prompting does not make models smarter or more truthful. This article explains what prompts actually change under the hood, why small edits cause big differences, and how engineers should think about prompting in production systems.

3. 3 Why Models Hallucinate (And Why That's Expected)

   Hallucination is not a bug in large language models but a predictable outcome of probabilistic text generation. This article explains why hallucinations happen, when they become more likely, and how engineers should design around them.

4. 4 Choosing the Right Model for the Job

   There is no universally best AI model. This article presents a production-minded approach to model selection, focusing on trade-offs, system requirements, and strategies for switching and fallback.

Make prompts stable, testable, and safe to integrate with systems.

1. 1 Prompt Structure Patterns for Production

   Prompts used in production must behave like interfaces, not ad hoc text. This article introduces proven prompt structure patterns that improve reliability, debuggability, and long-term maintainability.

2. 2 Output Control with JSON and Schemas

   Free-form AI output is fragile in production. This article explains how to use JSON and schema validation to make LLM outputs safer, more predictable, and easier to integrate with deterministic systems.

3. 3 Debugging Bad Prompts Systematically

   When AI outputs fail, random prompt tweaking is not debugging. This article presents a systematic methodology for identifying, reproducing, and fixing prompt-related failures in production systems.

4. 4 Prompt Anti-patterns Engineers Fall Into

   Many prompt failures come from familiar engineering anti-patterns applied to natural language. This article identifies the most common prompt anti-patterns and explains why they break down in production.

Build grounded AI with retrieval, ranking, and measurable quality.

1. 1 Why RAG Exists (And When Not to Use It)

   RAG is not a universal fix for AI correctness. This article explains the real problem RAG addresses, its hidden costs, and how to decide whether retrieval is justified for a given system.

2. 2 Chunking Strategies That Actually Work

   Effective chunking is an information architecture problem, not a text-splitting task. This article covers practical chunking strategies that improve retrieval accuracy in real-world RAG systems.

3. 3 Retrieval Is the Hard Part

   Most RAG failures stem from poor retrieval, not weak models. This article explains why retrieval is difficult, how to improve it, and how to debug retrieval failures systematically.

4. 4 Evaluating RAG Quality: Precision, Recall, and Faithfulness

   Without evaluation, RAG systems cannot improve reliably. This article introduces practical metrics and evaluation strategies for measuring retrieval accuracy, answer grounding, and regression over time.