Advanced RAG Engineering: Build Production-Ready Enterprise

Capture, prepare, index, and operate enterprise content to fuel reliable rag systems; enforce governance, provenance, and access controls while tuning chunking, metadata, and multi-index retrieval.

Apply semantic chunking to create coherent, retrieval-ready evidence by preserving meaning and context, and use boundary-detection pipelines with metadata to govern scalable RAG retrieval.

Learn hierarchical and parent-child chunking to enable precise retrieval and contextual answer generation by expanding from child hits to parent context within an enterprise RAG workflow.

MetaTadde, versioning, deduplication, and lineage form the trust layer for retrieval in production, ensuring provenance, auditability, and governed content from ingestion to indexing.

Build a production-grade enterprise ingestion pipeline with multi-format loaders, source lineage, deterministic hashing, three chunking strategies, CPU-based semantic embeddings, and a catalog of versions and duplicates.

Explore lexical, sparse, and dense retrieval signals in production RAG systems, combining BM25-based exact-word matching with weighted term expansion and semantic vector search for balanced precision, recall, and governance.

Advanced RAG engineering uses candidate generation and result fusion across lexical, dense vector, and metadata retrievers to maximize recall, produce a deduplicated candidate set, then re-rank with governance and observability.

Normalize scores per retriever and apply reciprocal rank fusion to stabilize hybrid retrieval across enterprise queries, then blend signals with weights and monitor recall and NDCG.

Learn how metadata and security-aware filtering narrows retrieval before generation to ensure only authorized documents are searched, surfaced, and cited in enterprise RAG systems.

Retrieve broadly, then rerank top candidates with semantic, logical, freshness, and security signals, while applying metadata filters and token budgets to produce precise context.

Extend an enterprise ingestion platform with a complete hybrid retrieval layer that combines exact FTS-5 matches and Qdrant semantic search, then apply a cached CPU re-ranker to surface strongest evidence.

Demonstrates a production-ready hybrid retrieval pipeline that fuses lexical SQLite FTS5 BM25 with semantic quadrant embeddings, using reciprocal rank fusion and CPU cross-encoder re-ranking.

Identify retrieval-unfriendly queries and repair them by clarifying missing entity, time range, and scope, rewriting for corpus relevance, and routing to appropriate retrievers in an enterprise rag pipeline.

Rewrite user queries to improve retrieval quality while preserving the task, entities, and constraints. Apply guardrails and intent checks; use BM25 and hybrid retrieval to ground answers in evidence.

Multi-query generation expands a single user intent into paraphrase, keyword-focused, entity-focused, and constraint-aware variants across vector search and BM25, boosting recall while preserving intent.

Learn how to decompose complex user queries with a planner into focused subqueries to improve evidence retrieval, coverage, and attribution in enterprise RAC systems.

Learn how query classification and retrieval routing guide a rag system to select the right evidence path, from lexical search to graph and multimodal retrieval, for safer, cheaper results.

Build a production-ready query intelligence pipeline that classifies, rewrites, expands, and routes complex questions to lexical, vector, or hybrid retrieval, with strong safety guards and traceability.

Explore test query rewriting, multi-query retrieval, HyDE, and routing within a production-ready RAG pipeline, featuring conversational rewrite, vocabulary mismatch handling, and exact identifier routing validated by a pydentic schema.

Adaptive RAG shifts to a decision loop that classifies intent, rewrites queries, routes retrieval to the right sources, scores evidence, and balances quality with cost and latency.

Ensure answer verification functions as the safety layer, checking claims against retrieved evidence to deliver grounded answers or safe abstentions. Establish a modular guardrail to decide when to answer or abstain.

Implement controlled retries, loop limits, and cost controls in production RAG systems to bound attempts, optimize retrieval and verification, and abstain when evidence or budgets are exhausted.

Build a bounded self-correcting RAG workflow that grades retrieved context, repairs weak retrieval, generates from verified evidence, and abstains safely, using shared schemas and typed state.

Discover when graph RAG adds value by using relationships between entities for multi-hop reasoning and cross-document synthesis beyond simple chunk retrieval.

Bridge unstructured text with structured knowledge by extracting entities and relationships for enterprise rag, including evidence spans and confidence scoring.

Build a cpu-friendly enterprise graph frag layer, validating relationships, conservatively resolving names and aliases, traversing bounded multi-hop paths, preserving provenance, and fusing graph evidence with the vector retriever.

Learn to build a production-ready enterprise RAG pipeline with multi-hop and hybrid graph retrieval, combining vector evidence and graph paths, alias resolution, and bounded traversal for grounded answers.

Analyze how multimodal enterprise documents—text, tables, charts, captions, and metadata—are parsed, preserved with evidence, permissions, and document structure, and indexed to yield grounded, retrieval-ready context for enterprise search.

Learn how layout-aware parsing enhances enterprise RAG by linking OCR text to regions, tables, headers, and captions, preserving reading order and coordinates for accurate, auditable retrieval.

Master image, diagram, and page-level retrieval to preserve layout, connect visual regions to captions and ocr text, and deliver precise, citable evidence from multimodal enterprise documents.

Explore CPU-only multimodal retrieval that turns PDF pages into grounded text evidence, uses OCR selectively, preserves tables and figure labels, and returns source-page citations.

Explains a cpu-only multimodal retrieval pipeline that extracts tables, captions, and page-level context from PDFs, with OCR and visual citations, using lexical and vector retrieval for rag.

Begin with an evaluation-first approach that defines questions, evidence, failure cases, and metrics before building the RAG pipeline for production, then run a repeatable evaluation loop to drive targeted improvements.

Balance golden and representative datasets to assess correctness and real-world behavior, using annotated, versioned data with evidence and metadata for a repeatable engineering feedback loop.

Evaluate final answers with generation and grounding metrics for faithfulness and relevance. Link claims to evidence, rate citation accuracy, and drive repairs or escalation.

Evaluate how citation and source evaluation ground every claim to evidence in a production RAG system, using metadata, claim-to-evidence mapping, and an audit trail to ensure reliability.

Leverage synthetic evaluation to predefine seed questions, expected evidence, and rubrics for a versioned test suite that detects regression in a production RAG system, guiding release gates.

Build a deterministic local regression evaluation workflow that compares four configurations against a golden dataset without requiring a model judge.

In this demo, compare rag configurations using quality, latency, and cost metrics with a fully local evaluation harness, contrasting baseline vector, hybrid, hybrid with re-ranking, and query enhanced retrieval.

Define production budgets for latency, cost, quality, and risk in a RAG system. Link budgets to routing, retrieval, and generation decisions and translate them into SLOs and observability.

Understand cost per query as the unit economics of a RAG system, tracing retrieval, re-ranking, context construction, and generation to optimize cost and quality in production.

Build and measure a production-ready RAG pipeline: instrument the platform, add performance observability, implement a persistent retrieval cache, deduplicate context, and compare baseline and optimized profiles while preserving answer quality.

Trace, diagnose, and optimize a complete rag request in this lab demo, comparing original and optimized profiles with caching, cross-encoder re-ranking, and retrieval optimizations to preserve answer quality.

Classify the data first and approve sources before retrieval, enforcing governance with pre-retrieval filters and an audit trail to ensure only approved knowledge feeds the model.

This lecture explains ACL-aware retrieval and security trimming. It shows how identity, entitlements, and metadata filters shape retrieval to prevent unauthorized content and ensure compliant citations.

Learn how prompt injection and retrieval poisoning threaten production RAG systems, and implement secure end-to-end defenses—before generation, during retrieval, and at the answer boundary.

Build a complete, immutable lineage and audit trail for production rag systems, enabling reproducible results through captured sources, prompts, model versions, and evidence records.

Freshness, retention, and deletion shape trusted RAG systems by tracking ingestion, updates, and removal across embeddings, caches, and audit logs to form a governed knowledge pipeline.

Orchestrate production deployment for a RAG system with versioned artifacts and quality gates. Use a CI/CD workflow that tests retrieval quality, grounding, latency, and cost, with canaries and precise rollbacks.

Evaluate enterprise rag production-readiness with a definitive decision, a clear responsibility model, and a minimum checklist covering access, evidence, traces, rollback, and owners—explainable, controlled, observable, and reversible.