Tag: llm
Browse 23 practical software engineering resources tagged with "llm". Discover code recipes, design patterns, documentation templates, and in-depth guides to help you build, deploy, and maintain production-ready solutions involving llm. Each resource is written for engineers who ship real systems, with copy-paste examples and practical trade-offs.
Large Language Models in Applications
Large language models enable applications that understand, generate, and transform text. Production LLM systems require prompt engineering, context management, evaluation, cost tracking, and safeguards against hallucinations.
The resources below cover prompt design, retrieval-augmented generation, function calling, evaluation metrics, local models, and cost optimization. Each recipe shows how to integrate LLMs into real products responsibly.
Every resource includes clear explanations, copy-paste code, and practical warnings. Use them to make informed decisions, avoid production pitfalls, and speed up your delivery. If you are just getting started, read the beginner-friendly articles first; if you are experienced, jump straight to the advanced patterns and architecture guides. New resources are added regularly, so bookmark this page and check back for the latest patterns.
Build Autonomous AI Agents with Tool Use and Reasoning
How to design AI agents that autonomously plan, execute tools, and iterate toward goals using...
OpenAI Assistants API Chatbot: Build, Cost & Deploy
How to create an AI chatbot using the OpenAI Assistants API with function calling and file search.
Generate Images Programmatically with AI Models
How to create, edit, and optimize images using DALL-E, Stable Diffusion, and Midjourney APIs with...
Fine-Tune a Language Model for Code Generation
How to fine-tune an LLM for code using LoRA and QLoRA on consumer GPUs.
Apply Prompt Engineering: What Works
How to write useful prompts for LLMs using role assignment, few-shot examples, chain-of-thought...
Compose LCEL Chains in LangChain for Multi-Step LLM
Build composable LLM pipelines with LangChain Expression Language (LCEL) using pipes, parallel...
Build a RAG Pipeline with LangChain and Vector Databases
How to build a Retrieval-Augmented Generation (RAG) pipeline using LangChain and vector databases...
Build a Slack Bot with OpenAI GPT-4
How to build a conversational Slack bot powered by OpenAI GPT-4 that responds to mentions and...
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...
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 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,...