“rag”
Building an AI Research Assistant End-to-End
Go from a prompt to a working research assistant: ingest papers and sources, index them for retrieval, let an agent plan and answer, and surface citations you can trust.
Building a Production-Ready RAG Pipeline with LlamaIndex
Move beyond the demo RAG app. Learn how to structure a production RAG pipeline with LlamaIndex: ingestion, chunking, embedding, retrieval, evaluation, and observability.
AI-Native Databases: Auto-Embeddings and Data APIs
The AI-native database keeps embeddings and your data in sync without a separate pipeline. Learn the trigger + queue + edge-function pattern on Supabase pgvector, and when auto-embeddings beats a standalone vector store.
Web Scraping for RAG: Clean Data Extraction at Scale
Junk in, junk out. Learn to extract LLM-ready Markdown from JS-heavy sites with Playwright, strip boilerplate, and chunk the results for a RAG pipeline.
Knowledge Graphs for AI: GraphRAG Explained
Naive RAG misses the answer that connects many documents. GraphRAG builds a knowledge graph from text and queries it. Learn the indexing pipeline, community detection, and query modes.
LLM Application Architecture Patterns in 2026
The architecture of an LLM application is no longer one prompt to one model. Learn the RAG, agentic, and caching patterns that production teams actually ship in 2026.
Gemini 3 in LegalTech: Automating Complex Contract Audits with High-Precision Reasoning
Contracts are full of computational clauses that probabilistic models get wrong. Learn to build a production-grade contract audit system that pairs Gemini 3 extraction with a deterministic rule engine to eliminate the "reasoning cliff" and the hallucination risk.
Fine-Tuning vs. RAG: When to Use Which Technique
Fine-tuning changes how the model behaves; RAG changes what it sees. Use a checklist and a small offline eval to decide between the two — and when to combine them into a hybrid.
RAG Evaluation: Measuring Retrieval Quality Objectively
A RAG pipeline fails in two halves: retrieval and generation. Build a transparent Python eval harness computing hit_rate, MRR, context precision/recall, and faithfulness to know exactly which half to fix.
The "Continuous-Learning" Agent: Updating Gemini 3 Knowledge via Live Feeds
An LLMs knowledge freezes at training time. Learn the continuous-learning agent pattern: frozen base + live vector store, temporal scoring, EWC-guarded micro-adaptation, and indexes that learn from validated queries.
Multi-Modal RAG for Video: Using Gemini 3 to Build Searchable 4K Video Knowledge Bases
Transcription-only video search throws away the pixels. Learn to embed video natively with Gemini Embedding 2, chunk with overlap, truncate with Matryoshka, and return trimmed clips on a text match.
LLM as a Wiki: Why Your AI Needs a Librarian, Not Just a Memory
Exploring the "LLM Wiki" pattern: A shift from stateless RAG to persistent, structured knowledge bases managed by AI, as inspired by Kasidistoy and Andrej Karpathy.
Agentic RAG: Leveraging Gemini 3's Reasoning for Zero-Index Data Retrieval
How to move beyond traditional vector-based RAG using the reasoning capabilities of Gemini 3 for zero-index data retrieval.
Designing a Production-Ready RAG System with Flutter, Dart, Chroma, and dartantic_ai
Learn how to build a highly intelligent Retrieval-Augmented Generation (RAG) application in Flutter using Dart, the Gemini API, and a vector database for semantic search.
Build a "Chat with Your Docs" Bot Using RAG and LlamaIndex
Create an intelligent bot that can answer questions based on your own documentation using Retrieval-Augmented Generation (RAG) and LlamaIndex.
Beyond Basic RAG: Exploring Advanced Retrieval Techniques for AI Devs
Dive into advanced Retrieval-Augmented Generation techniques like query expansion, re-ranking, and hybrid search.
Using AI to Synthesize Knowledge in Organizations
Implementing a RAG AI Agent with an organization's internal database is a highly effective approach to managing and leveraging the vast amounts of data that organizations possess.