Rag

Introduction TL;DR: Pick the scenario first (summarize, extract, classify, generate, agent), then attach an output contract, constraints, and validation rules. Each example below uses System/Developer/User layering, a strict output format, and a sample “expected output shape”. Why it matters: Contracts and validation reduce variance more than “clever wording”. 1) Document Summarization with Preservation Rules Prompt template 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 [SYSTEM] You are a technical editor. Never guess; say "unknown" when unsupported. [DEVELOPER] Goal: Summarize the document. Constraints: - Max 7 sentences - Preserve numbers/dates/proper nouns verbatim - No speculation Output (Markdown): ## Summary - ... ## Key Facts - ... ## Open Questions - ... [USER] <document text> Example output shape 1 2 3 4 5 6 7 8 9 ## Summary - The document describes a change announced on 2025-12-01. - It affects 3 API v2 endpoints and 1 auth change. ## Key Facts - Token TTL changed from 3600s to 1800s. ## Open Questions - Deployment region is not specified in the document. Why it matters: “Shorter” alone increases hallucinations; preservation + unknown-policy keeps it safe. ...

Introduction TL;DR: Crawl4AI is an open-source web crawler and scraper specifically engineered for LLM applications like RAG and AI agents. Its primary innovation is transforming noisy web HTML into clean, LLM-ready Markdown format. Built on a Playwright-based asynchronous architecture, Crawl4AI offers high performance, robust browser control, and adaptive crawling logic. It is easily deployed via Docker or a Python library, significantly streamlining the Ingestion phase of AI data pipelines for practitioners. In the era of Generative AI, the demand for high-quality, up-to-date domain knowledge is critical for model performance. Crawl4AI, first introduced on GitHub (unclecode/crawl4ai), addresses this gap by providing a specialized tool for collecting data that is intrinsically optimized for Large Language Models. This guide provides an in-depth look at its features and practical usage for data engineers and machine learning developers. ...

Introduction TL;DR: AnythingLLM by Mintplex Labs is an open-source, privacy-first AI platform combining RAG, AI Agents, and multi-LLM orchestration in one desktop or Docker environment. It enables fully local AI workflows with support for various LLM providers and complete offline functionality. Key Features Local-first AI Platform AnythingLLM runs all processes locally by default — including the LLM, vector DB, and embeddings — ensuring data privacy and offline functionality. Why it matters: Enables fully private deployments without external API dependency. ...