E-commerce vector search: a shopper query runs through keyword and semantic retrieval, merged by hybrid ranking into filtered, ranked product results.

RAG & AI Search

By Hetal K
November 6, 2026

Vector Search for E-Commerce: Semantic Product Discovery, Ranking and Hybrid Retrieval

Design semantic product discovery with hybrid retrieval, explicit filters and relevance evaluation. Includes a tested ranking fixture and commerce-system guardrails.

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.

RAG & AI Search

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.

RAG & AI Search

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.

RAG & AI Search

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.

RAG & AI Search

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.

RAG & AI Search

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.

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.

RAG & AI Search

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.

RAG & AI Search

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.

RAG & AI Search

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.

Hybrid search: a keyword ranked list and a vector ranked list combined by reciprocal rank fusion into one hybrid result that keeps both exact terms and meaning.

RAG & AI Search

By Hetal K
August 11, 2026

Hybrid Search vs Vector Search vs Keyword Search for Enterprise RAG

Compare hybrid, vector and keyword retrieval for enterprise RAG with a ranking-fusion example, evaluation queries, precision, recall and operational trade-offs.

RAG & AI Search