Advanced LLM Systems

Production RAG • Fine Tuning • JSON Extraction • Multimodal Pipelines

Overview

This slide covers advanced components used to scale large language model systems into robust production environments, combining retrieval, customization, structured outputs, and multimodal reasoning.

Key Concepts

Production RAG

High‑reliability retrieval pipelines with vector search, ranking, and prompt‑level routing.

Fine Tuning

Efficiently optimize LLMs for domain‑specific tasks using small curated datasets.

JSON Extraction

Force structured outputs to power APIs, automation, and compliance‑safe workflows.

Multimodal Pipelines

Combine text, images, speech, or video for richer AI‑driven applications.

System Process

1. Ingest Data

2. Embed & Retrieve

3. Model Selection / Fine Tune

4. JSON‑Safe Output

5. Multimodal Post‑Processing

Use Cases

Enterprise search and knowledge automation
Regulated structured‑data extraction pipelines
Custom domain expert assistants
Vision‑enhanced workflows for documents or media

RAG vs Fine Tuning

RAG

Dynamic, up‑to‑date, no model retraining required.

Fine Tuning

High precision tasks with domain‑specific output behavior.

FAQ

When do I use RAG vs fine tuning?

Use RAG for knowledge updates; fine tuning for behavioral shaping.

Can JSON extraction fail?

Guardrails and schema enforcement greatly reduce errors.

Build Your Advanced LLM System

Combine RAG, fine tuning, structured outputs, and multimodal AI.