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Models & integrations · 28 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.
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.
A practical guide to fine-tuning language models on domain-specific data, covering dataset preparation, training strategies, evaluation methods, and deployment considerations for production systems.
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.
Learn practical techniques for detecting and mitigating bias in machine learning pipelines, from data collection through model deployment and monitoring.
Explore federated learning concepts, implementation patterns, and practical challenges of training machine learning models across distributed devices while preserving data privacy.
How to implement anomaly detection for application metrics — covering statistical methods, z-scores, moving averages, seasonal decomposition, and building alerting systems that catch real incidents without alert fatigue.
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.