Advanced LLM Systems
Production RAG · Fine Tuning · JSON Extraction · Multimodal Pipelines
Production RAG · Fine Tuning · JSON Extraction · Multimodal Pipelines
Modern LLM production systems combine retrieval-augmented generation, custom fine tuning, structured output, and multimodal processing to deliver high‑accuracy, scalable AI applications. Slide 108 highlights the intersection of these components and how they integrate into enterprise‑grade pipelines.
Combines vector search and LLM reasoning for reliable, grounded responses. Includes retrieval pipelines, chunking, embeddings, reranking, and latency optimization.
Improves model performance using domain‑specific data. Supports instruction tuning, supervised fine tuning, and adapter‑based approaches.
Structured output ensures predictable fields for API pipelines, enabling validation and downstream automation.
Integrates text, image, audio, and document understanding models into unified workflows.
Raw documents, images, and structured records enter the system.
Chunking, embedding, cleaning, and dataset construction.
RAG retrieval, fine‑tuned inference, multimodal analysis.
Validated JSON, reports, insights, and API responses.
General‑purpose, limited context, no grounding.
Grounded responses, updated knowledge, better accuracy.
Optimized, multimodal, structured, and highly reliable.
Often yes. RAG handles retrieval, but fine tuning improves reasoning and instruction following.
It ensures downstream systems receive predictable structured data.
Support for text, images, audio, and mixed‑format documents.
Accelerate development with reliable RAG, fine tuning, structured outputs, and multimodal AI.
Get Started