Blog
Blog
233 articles on building, governing, and measuring enterprise AI: agents, retrieval, workflows, security, MLOps, and department playbooks.
engineering · 1 min
Chunking Strategies: Fixed, Sentence, Semantic and Document-Aware
Chunking is where most RAG systems fail silently. Here are four chunking strategies, when each one wins, and how Bhogar AI auto-selects per document type.
April 18, 2026
engineering · 1 min
Designing Conditional Branches in AI Workflows
Conditional branches are where AI workflows turn from linear scripts into real automation. Here are the four patterns we use most often.
April 17, 2026
engineering · 1 min
Hybrid Search: Combining Dense Vectors with BM25
Pure vector search misses exact matches. Pure keyword search misses paraphrases. Hybrid search wins both. Here is how Bhogar AI implements it at scale.
April 17, 2026
engineering · 1 min
Loops in AI Workflows: Map, Reduce, While and For-Each
Loops are how workflows handle batches and lists. Here are the four loop patterns Bhogar AI ships and the failure modes each one prevents.
April 16, 2026
engineering · 1 min
Reranking: The Cheapest RAG Quality Win Most Teams Skip
A cross-encoder rerank step turns mediocre retrieval into great retrieval. Here is how Bhogar AI ships reranking with sub-100 ms latency.
April 16, 2026
engineering · 1 min
Parallel Branches: Saving Wall-Clock Time Without Saturating APIs
Parallel branches turn slow sequential workflows into fast ones - if you respect downstream rate limits. Here is the Bhogar AI pattern.
April 15, 2026
engineering · 1 min
Human-in-the-Loop: Approvals That Do Not Stall the Pipeline
Human approvals are essential for sensitive AI actions - but they kill throughput if designed badly. Here is the pattern Bhogar AI uses in production.
April 14, 2026
engineering · 1 min
Vector Stores Compared: pgvector, Pinecone, Weaviate, Qdrant
A clear, opinionated comparison of the four most-used vector stores in 2026 - and why Bhogar AI defaults to pgvector for most workloads.
April 14, 2026
engineering · 1 min
Error Handling and Retries in AI Workflows
AI workflows fail more often than CRUD apps. Here is how Bhogar AI handles errors, retries and dead-letter queues so production stays calm.
April 13, 2026
engineering · 1 min
Scheduled Triggers: Cron, Webhooks and Event Streams
A workflow only runs when something triggers it. Here are the trigger types Bhogar AI supports and the trade-offs of each.
April 12, 2026
engineering · 1 min
Sub-Workflows and Composition: DRY for AI Pipelines
Copy-pasting workflow logic does not scale. Sub-workflows turn shared steps into typed, versioned components - the right way to keep DRY in AI pipelines.
April 11, 2026
engineering · 1 min
Multimodal RAG: Indexing Images, PDFs and Tables Side-by-Side
Most enterprise knowledge lives in PDFs with charts and tables. Multimodal RAG actually understands them. Here is how Bhogar AI handles every format.
April 11, 2026
engineering · 1 min
Workflow Versioning and Rollback Without Downtime
Workflows change. Versioning and atomic rollback are the difference between a calm deploy and a war room. Here is how Bhogar AI ships them.
April 10, 2026
engineering · 1 min
Citations and Source Attribution: Building Trust in RAG Answers
Citations turn RAG from a confident hallucinator into a trustworthy assistant. Here is how Bhogar AI implements verifiable per-claim citations.
April 10, 2026
engineering · 1 min
Announcing Go & Java SDKs: BhogarAI Now Supports 4 Languages
Expanding our SDK ecosystem with idiomatic Go and Java clients - complete with retry, streaming, and type safety.
April 10, 2026
engineering · 1 min
Event-Driven AI: Wiring Workflows to Kafka, SQS and Pub/Sub
Event-driven AI scales beyond what request/response can. Here is how Bhogar AI consumes Kafka, SQS and Pub/Sub safely and at scale.
April 9, 2026
engineering · 1 min
Keeping RAG Fresh: Incremental Indexing and Change Capture
A stale RAG system is a worse-than-useless RAG system. Here is how Bhogar AI keeps your knowledge base fresh without rebuilding everything every night.
April 9, 2026
engineering · 1 min
Batch ETL with AI Steps: Patterns That Hold at Scale
Adding LLM steps to nightly ETL sounds easy and quietly fails at scale. Here is how Bhogar AI keeps batch AI pipelines reliable and predictable.
April 8, 2026
engineering · 1 min
Multilingual RAG: Building Knowledge Bases That Cross Languages
Global customers ask in their language; your docs are in English. Multilingual RAG bridges the gap. Here is how to ship it without doubling your indexing bill.
April 8, 2026
engineering · 1 min
Workflow Observability: Tracing Every AI Step
AI workflows fail in surprising ways. Distributed tracing turns surprise into evidence. Here is how Bhogar AI traces every step end-to-end.
April 7, 2026
engineering · 1 min
Query Rewriting: The Easiest 10-Point Recall Boost in RAG
Users ask short, ambiguous questions. Query rewriting makes them retrievable. Here is how Bhogar AI ships query rewriting with zero added latency.
April 7, 2026
engineering · 1 min
RAG Evaluation: Metrics That Actually Predict Production Success
Recall@k is necessary but insufficient. Here are the four metrics Bhogar AI tracks per knowledge base - and the eval harness that calculates them automatically.
April 6, 2026
engineering · 1 min
Semantic Caching: Cutting RAG Latency and Cost by 40%+
Semantic caches return cached answers for paraphrased questions. Here is how Bhogar AI implements semantic caching with consistency guarantees.
April 5, 2026
engineering · 1 min
Idempotency in AI Workflows: Why It Matters and How to Get It Right
Re-runs happen - retries, replays, manual reruns. Idempotent workflows survive them gracefully. Here is the pattern Bhogar AI uses everywhere.
April 4, 2026