“llamaindex”
Trajectory Scoring: Rewarding Good Plans, Not Just Answers
Move beyond answer-level evaluation with trajectory scoring that rewards agents for good reasoning, efficient planning, and sound decision-making throughout their execution.
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.
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.
Event-Driven Orchestration: Coordinating Agents over Message Buses
Build asynchronous, event-driven multi-agent systems using message buses like Kafka or RabbitMQ with LlamaIndex Workflows.
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.
Episodic Memory in Agents: Recording Sessions as Replayable Experiences
Learn how episodic memory enables AI agents to record, store, and replay entire sessions — turning one-off interactions into reusable, learnable experiences.
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.
Event-Driven Agent Workflows with LlamaIndex
Build production-grade event-driven agent workflows using LlamaIndex Workflows, with practical examples of async orchestration, event handling, and multi-agent pipelines.
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.
A Developer's Showdown: LangChain vs. LlamaIndex vs. Autogen
A comprehensive comparison of the top AI agent and application frameworks.
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.