Production RAG • Fine Tuning • Evaluation • JSON Pipelines • Multimodal AI Apps
Modern LLM systems require more than prompt engineering. This guide introduces advanced production concepts used to reliably deploy, scale, and evaluate AI applications.
Retrieval-augmented generation with vector stores, chunking strategies, retrieval scoring, and latency‑optimized pipelines.
Model adaptation using techniques like QLoRA, SFT, DPO, and domain‑specific supervised datasets.
Automated evals for accuracy, faithfulness, relevance, safety, and structured output consistency.
Reliable schema enforcement, validation loops, and LLM-as-parser designs for structured data workflows.
Combining text, images, audio, and video to build powerful AI-driven interactive systems.
Collect, clean, chunk, and label data.
Embed text and store in vector DB.
Perform SFT, fine tuning, or LoRA.
Run automated quality and safety tests.
Optimize for latency and cost.
Reliable RAG systems over internal documents.
Structured action pipelines using JSON outputs.
Image, audio, and video-driven experiences.
Often yes. RAG provides context; fine tuning refines behavior.
Use schema validation loops and constrained decoding when possible.
Yes for many use cases, but latency and cost should be considered.
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