ai
Practical resources about ai for software engineers.
47 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 apps in production: RAGAS, LLM-as-judge, hallucination detection, human eval, A/B...
Complete Guide to LLM Prompt Engineering
Write prompts that produce reliable LLM output: patterns, few-shot, chain-of-thought, RAG...
Complete Guide to LLM Security
A practical playbook for securing LLM apps in production: prompt injection, jailbreaks, data...
Local LLM Deployment: Ollama, vLLM & llama.cpp
Deploy large language models locally and on-premise. Covers Ollama, vLLM, llama.cpp, quantization,...
OpenAI API Guide: Python Examples for Production
Production guide to the OpenAI API: Responses API, chat completions, streaming, function calling,...
Complete Guide to RAG in Production
Take RAG to production: chunking strategies, vector store selection, hybrid retrieval, reranking,...
Vector Databases in Practice: Embeddings & Similarity Search
Build your first vector search pipeline: embeddings, ANN indexes, similarity search with pgvector...
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