AverageDevs - Real-World Web Dev Guides for Working Developers
Techniques for Reducing Hallucinations in LLM Based Applications
A practical, engineering focused guide to diagnosing and reducing hallucinations in LLM based apps using prompt design, retrieval, constraints, evaluation, and architecture patterns.
Context Windows for LLMs: How to Optimize Prompts for Long Documents
A practical, engineering-focused guide to token limits, chunking, retrieval, compression, and prompt budgeting so your long-document LLM features stay fast, accurate, and affordable.
TOON: A Token Efficient JSON Alternative for LLMs
A pragmatic proposal for a token-efficient data notation that plays nicely with LLM tokenizers, with TypeScript encoders, streaming patterns, and safety practices.
Designing Architecture for AI‑Powered Recommendation Engines
A practical blueprint for building modern recommender systems - signals, retrieval, ranking, feedback loops, and evaluation with example snippets.
Explore how RAG works and how to implement it in a SaaS project
A deep, practical tutorial on Retrieval-Augmented Generation (RAG) for SaaS: architecture, ingestion, retrieval, reranking, compression, prompting, citations, evaluations, costs, and a typed Next.js/Node implementation with code snippets.
LangChain with Next.js to build context-aware chatbots
A practical, typed guide to build context-aware chatbots in Next.js with LangChain: chains, memory, retrieval (RAG), streaming, and production tips with TypeScript code.
Retrieval‑Augmented Generation (RAG): A Practical Guide for Production
What RAG is, when to use it, how it works under the hood, and concrete patterns to ship grounded, reliable LLM features in production.
Build a Document Q&A Bot with Next.js, TypeScript, and LangChain (Complete Guide)
A production-grade, end-to-end tutorial for building a document Q&A bot using Next.js (App Router), TypeScript, and LangChain - ingestion, embeddings, vector search, RAG chains, streaming, evaluations, and deployment tips.