#LlamaIndex

Articles about LlamaIndex — exploring patterns, best practices, and real-world implementations in production systems.

8 posts tagged with llamaindex. ← All posts

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Pratik Dhanave · ·4 min read

LlamaIndex in Production

A four-line RAG demo and a production RAG system share almost no operational concerns. Getting to production means treating ingestion as a pipeline, retrieval quality as something you measure, and the whole system as something you observe — the work that starts after the demo impresses everyone.

A four-line RAG demo and a production RAG system share almost no operational concerns. Getting to production means treating ingestion as a pipeline, retrieval quality as something you measure, and the whole system as something you observe.

#LlamaIndex#RAG#MLOps#Observability#AI Engineering#Python#Reliability
Pratik Dhanave · ·4 min read

Workflows

An agent's reasoning loop is flexible but opaque and hard to control. A workflow is the opposite: you make the orchestration explicit as steps and events, trading some autonomy for the predictability, testability, and control that complex applications need.

An agent's reasoning loop is flexible but opaque and hard to control. A workflow is the opposite: you make orchestration explicit as steps and events, trading some autonomy for the predictability, testability, and control complex applications need.

#LlamaIndex#AI Agents#AI Engineering#Software Architecture#RAG#Python#Multi-Agent Systems
Pratik Dhanave · ·4 min read

Agents and Tools

The moment you expose a query engine as a tool, RAG stops being a fixed pipeline and becomes a decision: the agent decides whether to retrieve, from which source, and whether one search was enough. That is agentic RAG, and it's built into LlamaIndex.

The moment you expose a query engine as a tool, RAG stops being a fixed pipeline and becomes a decision: the agent decides whether to retrieve, from which source, and whether one search was enough. That is agentic RAG.

#LlamaIndex#AI Agents#RAG#Tool Use#AI Engineering#Python
Pratik Dhanave · ·4 min read

Chat Engines and Memory

A query engine answers one question in isolation; a chat engine holds a conversation. The difference is memory — and handling memory well is what separates a demo chatbot from one that stays coherent and affordable over a long dialogue.

A query engine answers one question in isolation; a chat engine holds a conversation. The difference is memory — and handling it well is what separates a demo chatbot from one that stays coherent and affordable over a long dialogue.

#LlamaIndex#RAG#Memory#AI Engineering#Python
Pratik Dhanave · ·4 min read

Retrievers and Query Engines

A retriever finds the relevant Nodes; a query engine turns those Nodes into a grounded answer. Together they are the RAG pipeline — and the seams between them (postprocessing, response synthesis) are where you tune quality.

A retriever finds the relevant Nodes; a query engine turns those Nodes into a grounded answer. Together they are the RAG pipeline — and the seams between them are where you tune quality.

#LlamaIndex#RAG#AI Engineering#Python#Software Engineering
Pratik Dhanave · ·5 min read

Indexes and Embeddings

An index is the data structure that makes your Nodes findable, and for RAG that almost always means embeddings in a vector store — but LlamaIndex offers more than one index type, and knowing which organizes your data for which query pattern is the point.

An index is the data structure that makes your Nodes findable, and for RAG that almost always means embeddings in a vector store — but LlamaIndex offers more than one index type, and knowing which fits which query pattern is the point.

#LlamaIndex#RAG#Embeddings#AI Engineering#Python#Vector Databases
Pratik Dhanave · ·4 min read

Documents and Nodes

Everything LlamaIndex retrieves is a Node, and the quality of your Nodes — how you load your data and how you chunk it — sets the ceiling on everything downstream, no matter how good your model or retriever is.

Everything LlamaIndex retrieves is a Node, and the quality of your Nodes — how you load your data and how you chunk it — sets the ceiling on everything downstream, no matter how good your model or retriever is.

#LlamaIndex#RAG#AI Engineering#Python#Software Engineering
Pratik Dhanave · ·5 min read

What Is LlamaIndex?

LlamaIndex began as the fastest way to build RAG and has grown into a full data framework for LLM applications — connect your data, index it, retrieve it, and reason over it, with agents and workflows on top.

LlamaIndex began as the fastest way to build RAG and has grown into a full data framework for LLM applications — connect your data, index it, retrieve it, and reason over it, with agents and workflows on top.

#LlamaIndex#RAG#AI Engineering#Python#Software Engineering#LLMs

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.