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14 articles
Practical patterns for building reliable, cost-efficient LLM-powered features — prompt engineering, streaming, structured outputs, fallbacks, and cost management.
Build production RAG pipelines that retrieve relevant context and generate accurate answers, covering chunking strategies, embedding models, vector search, re-ranking, and evaluation techniques that separate useful RAG from hallucinating chatbots.
Build a practical code review assistant that integrates with your CI pipeline, uses LLM APIs to analyze pull requests for bugs, style issues, and security vulnerabilities, and posts actionable comments directly on PRs.
A practical guide to building production RAG systems that deliver accurate, grounded answers, covering embedding strategies, vector database selection, chunking approaches, reranking, and evaluation frameworks for measuring retrieval quality.
A practical guide to building evaluation pipelines for LLM-powered features covering semantic similarity metrics, behavioral testing, regression detection, and automated quality gates.
A practical guide to crafting prompts that produce reliable, production-quality code from language models, covering structured prompting, few-shot examples, chain-of-thought reasoning, and evaluation strategies.
A hands-on guide to building reliable RAG systems, covering chunking strategies, embedding models, vector stores, and the retrieval patterns that separate working prototypes from production systems.
Fine-tune language models on your domain's data using parameter-efficient techniques like LoRA and QLoRA, with practical guidance on dataset preparation, training strategies, and evaluation methods that produce useful specialists without massive compute budgets.
Build effective RAG pipelines with practical TypeScript examples covering document chunking strategies, embedding generation, vector search tuning, prompt construction, and evaluation methods that measure retrieval quality.
Systematic prompt engineering patterns for production LLM applications including structured output extraction, chain-of-thought reasoning, few-shot calibration, prompt versioning, and automated evaluation pipelines.
Design systematic evaluation pipelines for LLM-powered applications covering automated metrics, human evaluation protocols, regression testing, prompt versioning, and production monitoring for output quality.
Design and implement retrieval-augmented generation pipelines that go beyond simple demos, covering chunking strategies, embedding optimization, reranking, and evaluation metrics for production AI applications.
How to integrate LLMs into your code review workflow — covering automated review bots, prompt patterns for catching bugs, security analysis, and balancing AI suggestions with human judgment.
Practical prompt engineering techniques that turn LLMs into reliable coding assistants — covering context management, chain-of-thought, and structured outputs.