Tags
19 articles
Practical patterns for building reliable, cost-efficient LLM-powered features — prompt engineering, streaming, structured outputs, fallbacks, and cost management.
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.
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.
Compare vector database options including Pinecone, Weaviate, Qdrant, Milvus, and pgvector for AI applications, analyzing indexing strategies, query performance, filtering capabilities, and operational complexity trade-offs.
Design systematic evaluation pipelines for LLM-powered applications covering automated metrics, human evaluation protocols, regression testing, prompt versioning, and production monitoring for output quality.
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.
A practical guide to vector databases for similarity search — covering embeddings, indexing strategies, distance metrics, and when to use Pinecone, Weaviate, pgvector, or FAISS for your use case.
How to build production-ready NLP pipelines — covering text preprocessing, tokenization, embeddings, classification, and entity extraction with practical TypeScript and Python examples.
A practical introduction to recommendation systems — covering collaborative filtering, content-based filtering, and hybrid approaches with TypeScript implementations developers can understand and adapt.
A developer-friendly walkthrough of the Transformer architecture that powers GPT, BERT, and modern LLMs — covering self-attention, positional encoding, and the encoder-decoder structure with code examples.
A practical introduction to reinforcement learning concepts — agents, environments, rewards, and policies — with TypeScript examples that map RL ideas to problems developers already understand.
A beginner-friendly guide to computer vision fundamentals using Python and OpenCV — covering image processing, edge detection, object detection, and building a practical image classifier.
How to integrate AI tools into your code review workflow — covering automated suggestions, security scanning, review prioritization, and maintaining human judgment.
A practical introduction to natural language processing concepts web developers need — tokenization, sentiment analysis, named entity recognition, and integrating NLP APIs into applications.
Practical prompt engineering techniques that turn LLMs into reliable coding assistants — covering context management, chain-of-thought, and structured outputs.
A practical introduction to machine learning concepts that backend engineers need when integrating ML models into production applications.