Advanced LLM Systems
Production RAG, Fine Tuning, JSON Extraction, and Multimodal AI Pipelines
Production RAG, Fine Tuning, JSON Extraction, and Multimodal AI Pipelines
Modern LLM systems combine multiple components to deliver accurate, scalable, production-ready AI capabilities. This slide explores how Retrieval-Augmented Generation, fine tuning, structured outputs, and multimodal pipelines integrate into real workflows.
Retrieval-augmented systems that combine vector search, metadata filtering, and provenance tracking.
Improves model reasoning, style consistency, and domain adaptation using curated datasets.
Structured output generation for downstream automation and API integration.
Workflows combining text, images, audio, and video inputs for advanced AI-driven analysis.
Collect text, images, and structured data.
Embed and store chunks for high-precision retrieval.
Apply RAG, fine-tuned reasoning, and JSON schema constraints.
Produce structured responses and trigger pipelines.
RAG-powered knowledge retrieval with traceability.
JSON-based action outputs for workflow automation.
Interpret documents, diagrams, and datasets together.
Fine tuning improves reasoning, formatting, and domain consistency beyond what retrieval provides.
Models are guided with schemas and constrained decoding to output structured data reliably.
They allow processing images, documents, and text within the same workflow for richer analysis.
Combine retrieval, fine tuning, structure, and multimodal intelligence.
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