Deep dives across data, automation & AI

Implementing Hypothetical Document Embeddings (HyDE) from Scratch: Generating Synthetic Answers to Improve Sparse and Dense Retrieval

HyDE bridges the gap between how users ask questions and how documents store answers — by asking an LLM to hallucinate a plausible answer and using that embedding to search your corpus. This hands-on lesson walks you through building HyDE for both dense vector search and BM25 from scratch, including multi-hypothetical averaging and hybrid fusion.

Implementing Tool Result Validation and Error Recovery in LLM Function Calling Pipelines

Function calling pipelines fail in three distinct zones — and most tutorials only handle one of them. Learn how to build complete validation, typed error taxonomies, retry logic, and graceful degradation for production LLM agents that actually stay reliable.

Using AI to Summarize, Compare, and Extract Insights from Multiple Documents Simultaneously

Stop summarizing documents one at a time and hoping you catch what matters. This lesson teaches you how to design AI prompt workflows that do genuine comparative reasoning across multiple documents at once — surfacing contradictions, gaps, and structured insights that feed directly into decisions.

Choosing the Right Embedding Model: OpenAI, Sentence Transformers, and Cohere Compared for RAG Use Cases

Your embedding model determines what "similar" means in your RAG system — and it's the single biggest lever over retrieval quality. This lesson compares OpenAI, Sentence Transformers, and Cohere side by side with working code, a decision framework, and a hands-on evaluation harness you can run on your own data.

Embeddings Explained: What They Are, How They Work, and How to Generate Them with the OpenAI and Sentence Transformers APIs

Embeddings are the secret engine behind semantic search, RAG pipelines, and AI recommendation systems — but most explanations skip the intuition. This lesson builds your understanding from first principles and walks you through generating, comparing, and applying embeddings using both the OpenAI API and the open-source Sentence Transformers library.

How to Give AI the Right Context: Using Background Information, Constraints, and Examples to Get Accurate, Relevant Responses

Most AI prompts fail not because the model is incapable, but because the model doesn't have enough context to do its job. This lesson teaches you the three core ingredients — background information, constraints, and examples — that transform vague prompts into reliable, accurate AI output. Walk away with a practical framework you can apply to any AI tool, starting today.

Temporal Reasoning in RAG: Handling Document Freshness, Version Conflicts, and Time-Sensitive Retrieval in Production

Most RAG systems are semantically blind to time — they'll happily surface a two-year-old policy over the current one if the embedding scores align. This expert-level lesson teaches you how to build temporal metadata schemas, freshness-weighted scoring, version conflict resolution, and query-intent classifiers that make your RAG system genuinely time-aware.

Implementing LLM-as-Judge Evaluation Pipelines: Automated Quality Scoring, Rubric Design, and Calibration Against Human Ratings for Production LLM Systems

LLM-as-Judge lets you evaluate thousands of AI responses automatically — but only if your rubric is sharp, your judge is calibrated, and your sampling strategy is deliberate. This lesson teaches you to build evaluation pipelines that actually predict human judgment, with production-grade code, calibration statistics, and regression detection.

Latency-Aware Prompt Design: Optimizing AI Response Speed for Real-Time Business Applications

Most AI latency problems aren't infrastructure problems — they're prompt design problems. Learn how to architect prompts, model routing strategies, and caching layers that keep real-time AI applications under 2 seconds, even for complex multi-step tasks.

Fine-Tuning Embedding Models on Domain-Specific Data to Improve Retrieval Accuracy in RAG Pipelines

Generic embedding models don't understand your domain's vocabulary, synonyms, or conceptual relationships — and that gap silently kills retrieval accuracy. This lesson teaches you how to fine-tune a sentence transformer on your own data, from synthetic training pair generation to quantitative evaluation and production deployment.

Implementing Query Decomposition and Sub-Question Synthesis for Complex RAG Pipelines*

How to Write an Effective AI Brief: Defining Goals, Constraints, and Success Criteria Before You Prompt

Most AI interactions fail not because of bad prompts, but because of unclear thinking before the prompt is written. Learn how to build an AI Brief — a five-section framework that defines your goals, audience, format, constraints, and success criteria — so every prompt you write starts from a position of clarity.