LLMOps architecture: a versioned release manifest flows through software checks, AI evaluation, a 5% canary and full rollout, with an owned one-command rollback and monitoring of behavior, evidence, latency and cost.

Cloud & MLOps

By Hetal K
August 21, 2026

LLMOps Architecture: CI/CD, Model Evaluation and Deployment for Enterprise LLM Apps

Design an enterprise LLMOps pipeline with versioned prompts, evaluation gates, staged deployment, monitoring and rollback ownership. Includes a release manifest.

RAG troubleshooting: a missing document traced through ingestion, parsing, permissions, retrieval, ranking and context assembly, with the root cause found at retrieval and saved as a regression case.

Generative AI & Agents

By Hetal K
August 20, 2026

RAG Production Troubleshooting: Why Retrieval Misses Documents and How to Fix It

Diagnose missing RAG evidence across ingestion, parsing, permissions, ranking and context assembly. Includes a synthetic trace, runbook and regression checklist.

RAG vs long-context LLMs: retrieval sends a small prompt of relevant passages with fresh sources and per-user permissions, while long context sends a large slice of the corpus at higher latency and cost.

Generative AI & Agents

By Hetal K
August 19, 2026

RAG vs Long-Context LLMs: Latency, Accuracy and Cost at Enterprise Scale

Compare RAG and long-context models for enterprise tasks: evidence coverage, changing sources, permissions, latency and cost, with a decision flow and examples.

Multimodal RAG: a PDF page with a table, a diagram and text, each converted to a representation that keeps its structure, linked by shared metadata such as page, source region, access list and units.

Generative AI & Agents

By Hetal K
August 18, 2026

Multimodal RAG Architecture for PDFs, Tables, Images and Diagrams

Design multimodal RAG for PDFs, tables, images and diagrams. Preserve structure, units, permissions and source regions, with worked examples and evaluation checks.

RAG cost optimization: an illustrative cost per 1,000 questions split across embeddings, vector search, reranking, generation and cache misses, with levers such as re-embedding only changed chunks and token budgets.

Generative AI & Agents

By Hetal K
August 17, 2026

RAG Cost Optimization: Embeddings, Reranking, Caching and Token Budgets

Optimize RAG costs across embeddings, reranking, caching and token budgets. Includes a downloadable calculator with explicit assumptions and quality checks.

Graph RAG vs traditional RAG: passage-centered retrieval of similar text chunks compared with a graph of supplier, part, product and customer entities whose relations form a multi-hop evidence path.

Generative AI & Agents

By Hetal K
August 16, 2026

Graph RAG vs Traditional RAG: Knowledge Graphs, Accuracy and Trade-Offs

Compare graph-assisted and passage-centered RAG through architecture flows, entity-linking risks, workload examples and a practical selection matrix.

Permission-aware RAG: a verified user identity filters retrieved documents by access list so only permitted chunks reach the prompt, with permission-keyed caches and revocation tests.

AI Security

By Hetal K
August 15, 2026

Enterprise RAG Security: Document-Level Permissions and Access-Controlled Retrieval

Design permission-aware RAG with trusted identity, document-level authorization, cache boundaries and revocation tests. Includes a runnable policy example.

Choosing a vector database: an evaluation matrix for hybrid retrieval, document-level filters, deployment, operating effort and cost to fill with your own tests, beside a total-cost worksheet.

Generative AI & Agents

By Hetal K
August 14, 2026

Azure AI Search vs Pinecone vs Weaviate: Choosing a Vector Database

Compare Azure AI Search, Pinecone and Weaviate for enterprise RAG: hybrid retrieval, permissions, deployment, operating effort and a transparent cost worksheet.

RAG evaluation scorecard: retrieval recall, context, faithfulness and answer quality scored separately against a target, beside a gold test case with question, expected source and must-include terms.

Generative AI & Agents

By Hetal K
August 13, 2026

How to Evaluate RAG Systems: Faithfulness, Retrieval Recall and Answer Quality

Evaluate RAG with recall, precision, faithfulness and answer-quality scorecards. Build gold test cases and run a transparent fixture-based calculation.

RAG chunking strategies: fixed-size splits that cut through a table, semantic splits on topic shifts, and document-aware splits that keep headings and tables whole.

Generative AI & Agents

By Hetal K
August 12, 2026

RAG Chunking Strategies: Fixed Size, Semantic and Document-Aware Splitting

Compare fixed-size, semantic and document-aware RAG chunking with runnable boundary checks and guidance for policies, tables, PDFs and source code.