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.
Agent Tool Selection Pattern
Dynamically select which tools an LLM agent can use based on the task context. Reduce token usage...
Embedding Cache Pattern
Cache LLM embeddings to reduce API calls and cost. Store embeddings with a content hash key and...
Human-in-the-Loop Pattern
Pause LLM agent execution for human approval before high-impact actions. Route decisions to a...
LLM Fallback Pattern
Fall back to alternative LLM providers or models when the primary fails. Handle rate limits,...
LLM Guardrails Pattern
Validate LLM inputs and outputs with rules, classifiers, and content filters. Prevent prompt...
LLM Router Pattern
Route queries to different LLM models based on complexity, cost, and latency requirements. Classify...
Prompt Chaining Pattern
Chain multiple LLM calls where each step's output feeds the next step's input. Break complex tasks...
RAG Hybrid Search Pattern
Combine keyword (BM25) and semantic (vector) search to improve retrieval accuracy in RAG pipelines....
AI Agent Design Document Template
Document AI agent architecture, tools, memory, reasoning patterns, safety guardrails, evaluation...
AI Data Preparation Checklist
Checklist for preparing data for LLM and RAG systems: data collection, cleaning, chunking,...
AI LLM Cost Tracking Template
Track token usage and costs per feature, model, and user. Includes cost categories, pricing tables,...
AI LLM Incident Response Runbook
Operational runbook for LLM production incidents: hallucination events, model outages, cost spikes,...
AI LLM Prompt Template Library
A reusable prompt template library for common LLM tasks: summarization, extraction, classification,...
AI Model Selection Matrix
Compare LLM models by cost, latency, context window, accuracy, and use case. Includes decision...
AI Prompt Version Control Template
Version your LLM prompts with eval scores, change history, rollback support, and A/B testing....
AI RAG Evaluation Checklist
A checklist for evaluating RAG system quality: retrieval accuracy, generation faithfulness, context...
Complete Guide to AI Agents in Production
Build production AI agents. Covers agent architectures, tool use, planning, memory, multi-agent...
Complete Guide to LangChain in Production
Run LangChain in production. Covers chains, agents, memory, tools, LCEL, streaming, callbacks, RAG...
Complete Guide to LLM Application Architecture
Build production LLM applications end-to-end. Covers API layers, prompt management, streaming,...
Complete Guide to LLM Cost Optimization
Optimize LLM costs in production. Covers model routing, prompt compression, caching, batch API,...
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