Advanced LLM Systems
Production RAG • Fine Tuning • JSON Extraction • Multimodal AI Pipelines
Production RAG • Fine Tuning • JSON Extraction • Multimodal AI Pipelines
Modern LLM systems combine retrieval, model adaptation, structured output control, and multimodal reasoning to deliver production-ready AI capabilities. Slide 113 highlights the layered architecture required to scale these systems.
Reliable retrieval pipelines with chunking, embeddings, ranking, and safety layers.
Task‑specific optimization using supervised fine tuning or preference tuning.
Structured output formats for safe parsing, automations, and API pipelines.
Image, text, audio, and PDF reasoning combined with tool use and workflow orchestration.
Collect text, images, PDFs, and external knowledge sources.
Embed, search, rank, and filter high‑relevance context.
LLM reasoning, fine tuning, and structured JSON output.
Pipeline automation, tools, agents, and post-processing.
RAG-powered insights on internal documents with traceability.
Structure data and outputs with JSON workflows.
Process images, charts, and PDFs alongside text instructions.
Most production systems benefit from using both in combination.
With schemas, validators, and constrained decoding, it becomes highly stable.
Many state-of-the-art LLMs now support images natively.
Take the next step with production-grade retrieval, tuning, and multimodal workflows.
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