ai
Practical resources about ai for software engineers.
48 results
Integrating AI and Machine Learning
Artificial intelligence has moved from research labs to production pipelines. LLMs, vector databases, and embedding-based search are now standard tools for building intelligent applications that understand context and generate content.
Explore recipes for building RAG pipelines, fine-tuning models, engineering prompts, and implementing semantic search. Each example focuses on practical integration rather than theory, with code you can adapt to your own data and use case.
Complete Guide to LLM Evaluation
Evaluate LLM applications in production. Covers RAGAS, LLM-as-judge, human evaluation, A/B testing,...
Complete Guide to LLM Prompt Engineering
Write effective prompts for AI models. Covers prompt patterns, few-shot learning, chain-of-thought,...
Complete Guide to LLM Security
Secure LLM applications in production. Covers prompt injection, jailbreaks, data leakage, OWASP Top...
Complete Guide to Local LLM Deployment
Deploy LLMs locally and on-premise. Covers Ollama, vLLM, llama.cpp, LM Studio, model quantization,...
Complete Guide to OpenAI API Mastery
Master the OpenAI API in production. Covers chat completions, streaming, function calling,...
Complete Guide to RAG in Production
Build production RAG systems. Covers chunking strategies, embedding models, vector stores,...
Complete Guide to Vector Databases
Compare and use vector databases in production. Covers Pinecone, Weaviate, Chroma, pgvector,...
Vector Databases — AI/ML Embeddings and Similarity Search
A practical guide to vector databases: embeddings, similarity search, approximate nearest...
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