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, tuning, structured outputs, and multimodal reasoning. This page summarizes the essential components used in real-world AI pipelines inspired by slide 106.
Combines retrieval engines with LLMs for grounded responses. Includes chunking, vector search, reranking, and caching.
Adapts models for domain language, tasks, or output style. Useful when prompt engineering alone is insufficient.
Ensures predictable structured outputs for APIs, automation, or downstream processing.
Blend text, images, data, and audio to enable richer contextual reasoning in enterprise applications.
Collect and prepare structured/unstructured sources.
Embed, index, and query relevant context.
Model generates grounded or tuned outputs.
Return JSON or multimodal results for apps.
Use RAG + extraction to produce structured summaries, audits, or answers.
Scale internal search and reasoning with tuned models.
Analyze PDFs, images, diagrams, and text together.
Not always. RAG often eliminates the need unless extreme precision is required.
Anytime automation, integrations, or downstream processing is needed.
Real-world data spans text, images, documents, and audio; multimodal systems improve accuracy and reasoning.
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