“rag”
Multi-Hop Retrieval: Answering Questions That Span Multiple Sources
How multi-hop retrieval extends RAG with an iterative retrieve, reason, and re-query loop, and how to implement it as a bounded LangGraph workflow.
Structured vs. Unstructured Retrieval: Documents, APIs, and Databases
Compare structured and unstructured retrieval strategies for agent RAG systems, with patterns for choosing between SQL, vector search, API calls, and hybrid approaches.
Context Budgeting in Agent RAG: What to Include, What to Drop
Master context window management for agent RAG systems with strategies for token budgeting, relevance scoring, compression, and dynamic context allocation using Gemini API patterns.
Tool-Augmented Retrieval: Chaining Search Tools with Agent Reasoning
Build retrieval systems where agents chain multiple search tools—vector, keyword, database, and API—using reasoning to decide what to search and when to stop.
Grounded Agents: Citation-Anchored Answers in RAG for Trustworthy AI
Build RAG agents that cite their sources with grounded answers, verifiable claims, and transparent attribution for production AI systems.
Re-Ranking Retrieved Context Before Injection: Precision Over Recall in RAG
Improve RAG accuracy by re-ranking retrieved documents with cross-encoders, semantic similarity, and diversity-aware ranking before injecting into LLM context.
Query Rewriting and Expansion for Agentic Retrieval: Better Search Starts Here
Transform vague user queries into precise retrieval targets using query rewriting, expansion, and decomposition techniques for agentic RAG systems.
Semantic Recall: Making Agent Memory Searchable with Embeddings
Transform agent memory from keyword-matching to semantic understanding. Use embeddings to let agents find relevant past experiences by meaning, not just keywords.
Long-Term Knowledge Stores: Moving Beyond Rolling Chat History
Stop treating agent memory as a sliding window. Learn how to build long-term knowledge stores that persist insights, facts, and context across thousands of interactions.
Hybrid Search for Agent Context: Dense, Sparse, and Keyword
Why hybrid search combining dense vector retrieval and sparse BM25 is essential for agent context, with a three-stage pipeline architecture and code examples.
Episodic Memory in Agents: Recording Sessions as Replayable Experiences
Learn how episodic memory enables AI agents to record sessions as replayable experiences, with implementation patterns using LlamaIndex and production architectures.
Agentic Retrieval: Letting the Agent Decide What to Fetch
Move beyond static RAG with agentic retrieval. Let agents decide what to search, when to stop, and how to combine information from multiple sources.
Agent Memory: Vector Stores, Knowledge Graphs, and Long-Term Recall
Deep dive into agent memory systems using vector stores, knowledge graphs, and hybrid approaches. Learn how to build agents that remember context across conversations with LlamaIndex.
Transforming a Reactive LLM Chatbot into an Agentic System
An architectural roadmap for moving from a single-turn, reactive LLM chatbot to a smart autonomous agent: planning loops, multi-tiered memory, multi-agent delegation, dynamic tool retrieval, and self-healing.
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.