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Blog
233 articles on building, governing, and measuring enterprise AI: agents, retrieval, workflows, security, MLOps, and department playbooks.
engineering · 1 min
Parent-Document Retrieval: Small Chunks, Big Context
Retrieve on small chunks for precision, return parent documents for context. The technique Bhogar AI uses to make RAG answers feel complete.
April 4, 2026
engineering · 1 min
File Processing Workflows: From Inbox to Indexed Knowledge
Files arrive in inboxes, drives and S3 buckets. Bhogar AI workflows ingest, parse, classify and route them - turning chaos into searchable knowledge.
April 2, 2026
engineering · 1 min
Contextual Compression: Filtering Retrieved Chunks Before Generation
Most retrieved chunks are noisy. Contextual compression filters them down to what is actually relevant - saving tokens and lifting answer quality.
April 2, 2026
engineering · 1 min
Reciprocal Rank Fusion vs Weighted Sum: Picking a Hybrid Fuser
When fusing dense and sparse results, RRF and weighted sum behave differently. Here is which one to pick - and why Bhogar AI defaults to RRF.
April 1, 2026
engineering · 1 min
Confidence Scoring: Knowing When Your RAG Answer Is Weak
A RAG system that knows when it does not know is dramatically more useful than one that always sounds confident. Here is how to score and act on confidence.
March 31, 2026
engineering · 1 min
Why You Need an LLM Gateway in 2026
Calling OpenAI directly from your app worked in 2023. In 2026 it is technical debt. Here is what an LLM gateway gets you and why Bhogar AI ships one by default.
March 29, 2026
engineering · 1 min
Cost-Aware Model Routing: Cheap Where Possible, Smart Where Needed
Most LLM calls do not need the flagship model. Cost-aware routing sends easy queries to small models and hard queries to large ones - automatically.
March 28, 2026
engineering · 1 min
Provider Fallback: Surviving Outages Without Pages
Every major LLM provider had a multi-hour outage in the last 12 months. Fallback turns those outages into rounding errors instead of incidents.
March 27, 2026
engineering · 1 min
Prompt Caching at the Gateway: 80% Cost Cuts on Repetitive Workloads
Repetitive prompts are 60-90% of most production LLM workloads. Gateway-level caching turns that into immediate, measurable savings.
March 26, 2026
engineering · 1 min
Rate Limiting and Quotas Across Providers
Provider rate limits are the silent killer of AI features at scale. Centralised gateway rate limiting prevents one tenant from starving the rest.
March 25, 2026
engineering · 1 min
When Not to Use an Agent: Eight Workloads Where a Workflow Wins
Not every problem needs an agent. Here are the eight workloads where a deterministic workflow ships faster, costs less and runs more reliably.
March 25, 2026
engineering · 1 min
Streaming Responses Through the Gateway
Streaming makes AI feel fast. Gateway streaming preserves perceived latency while keeping observability and budgets intact.
March 23, 2026
engineering · 1 min
Structured Outputs: JSON Mode That Actually Validates
JSON mode is the difference between AI that integrates and AI that you have to babysit. Here is how Bhogar AI delivers schema-validated structured outputs across providers.
March 22, 2026
engineering · 1 min
Function Calling Across Providers: A Compatibility Layer
Each provider has its own function-calling API. The gateway gives you one. Here is how Bhogar AI normalises tool use across OpenAI, Anthropic, Google and open models.
March 21, 2026
engineering · 1 min
Observability for the LLM Gateway: What to Measure and Alert On
The four golden signals for an LLM gateway are not the same as for an HTTP service. Here is what to measure, what to alert on and what to ignore.
March 20, 2026
engineering · 1 min
Multi-Modal Through the Gateway: Vision, Audio and Documents
Vision, audio and document inputs each have their own provider quirks. The gateway hides them. Here is how Bhogar AI handles every modality uniformly.
March 19, 2026
engineering · 1 min
Open-Source Models Through the Gateway: vLLM, Ollama and TGI
Open-source models on your own GPUs are 5-20× cheaper for the right workloads. Here is how Bhogar AI lets you mix them with hosted providers.
March 18, 2026
engineering · 1 min
80+ Integrations: Connect BhogarAI to Your Entire Stack
From Slack to Snowflake, WhatsApp to Power BI - our integration ecosystem has grown to 80+ connectors.
March 18, 2026
engineering · 1 min
Prompt Versioning and Experimentation Through the Gateway
Prompts are code. They deserve versioning, A/B tests and rollback. Here is how Bhogar AI ships prompt management as a gateway service.
March 17, 2026
engineering · 1 min
LLMOps in 2026: The Discipline, Not the Tool
LLMOps is not a single product you buy. It is a discipline you adopt, with primitives across observability, eval, deploy and governance. Here is the 2026 picture.
February 25, 2026
engineering · 1 min
Distributed Tracing for LLM Workloads
A complete LLM trace covers prompt, retrieval, tool use and post-processing. Here is how Bhogar AI builds those traces and what to do with them.
February 24, 2026
engineering · 1 min
Building an Eval Pipeline That Catches Regressions
A good eval pipeline tells you a prompt change is bad before users do. Here is how Bhogar AI ships continuous evals.
February 23, 2026
engineering · 1 min
A/B Testing Prompts and Models in Production
Offline evals predict only so much. A/B tests in production close the loop. Here is how Bhogar AI ships traffic-split A/B for prompts and models.
February 22, 2026
engineering · 1 min
Canary Deploys for AI Workflows
Canary deploys catch problems before full rollout. Here is how Bhogar AI applies canary patterns to workflows, agents and prompts.
February 21, 2026