Model Governance: Who Approves What, And When?
Your models are making decisions. Do you know who signs off on those decisions, or when? Untamed model deployments are a liability waiting to happen.
Blog · Topic
AI Production
Keeping AI systems reliable in production: monitoring, evals, fallbacks, caching and the operational work most teams skip.
28 posts
Multi-modal RAG: Text Isn't Enough Anymore
Why your RAG system needs to ingest more than just text to deliver real value.
Orchestrating Multi-Model Pipelines: DAGs Are Just The Start
Stop treating multi-model AI pipelines as simple DAGs. Production demands dynamic orchestration that handles real-world complexity, not just theoretical flows.
Graph Databases for Complex Relationships
Why vector embeddings fail when entity relationships matter, and how adding Knowledge Graphs to your RAG pipeline fixes context fragmentation.
Semantic Caching: The Missing Layer in Your RAG Architecture
Why hitting the model API for every query is burning your budget and slowing down your users.
Scaling Inference: From POC to Production
Why your proof of concept's inference architecture breaks down at scale and how to fix it.
Why Your Batch Checks Are Too Late
Waiting for overnight batch jobs to catch silent schema drift or corrupted data is a recipe for broken downstream AI models. You need validation at the gate.
The Lineage Imperative: Why Your AI Needs a Data Family Tree
Stop treating data lineage as a compliance checkbox. For AI, it's the bedrock of trust, explainability, and defensibility. Without it, your models fly blind.
The Retraining Schedule That Doesn't Exist
Your model is degrading. You just don't know it yet. It's time to treat retraining as a core operational primitive, not a reactive fire drill.
Why Your LLM API Gateway Is Critical (and You Don't Have One)
Your LLM integrations are scaling. Without an API gateway, you're building a brittle, unobservable mess. It's not optional for production.
The Circuit Breaker Your LLM Integration Is Missing
Your LLM integration has no circuit breaker. That's not a minor gap — it's a reliability time bomb.
The Graceful Degradation Nobody Designed
Your AI system has a happy path and a crash path. The space between them is where real production lives.
The Timeout You Never Set
LLM API calls without explicit timeouts are a production incident waiting to happen. Here's what hangs, why, and how to stop it.
The Cost Spike You Didn't See Coming
Nobody models LLM costs seriously until they get the bill. By then, the architecture is already wrong.
The System Prompt That Grew Without Anyone Noticing
System prompt bloat is one of the slowest ways to degrade your LLM system — and one of the easiest to miss until performance tanks and costs spike.
Tool Calls Are Side Effects. Treat Them That Way.
Agents that call tools are running code with real consequences. Most teams build them like they're not.
The Retry Loop That Ate Your API Quota
Naive retry logic is one of the most common — and most expensive — bugs in LLM production systems. Here's what it looks like and how to fix it.
The Context Window Is Not a Clipboard
Bigger context windows didn't solve the problem of what goes in them. Most production LLM failures aren't model failures — they're context failures.
Structured Output Is Not a Nice-to-Have
If your LLM integration parses free-text responses in production, you don't have a product. You have a fragile prototype waiting to fail.
The Confidence Problem in LLM Outputs
LLMs don't know when they're wrong. Your production system has to.
Your LLM Has a Latency Budget. Do You Know What It Is?
Most teams ship AI features without defining acceptable latency. Then they spend months optimizing the wrong thing.
LLM Versioning: The Problem Nobody Solves Until It's Too Late
Your model changed under your app. Your prompt changed under your users. And nobody noticed until something broke. Fix this before it happens to you.
Your RAG Pipeline Needs Monitoring, Not Just Better Retrieval
Tuning chunk size and tweaking similarity thresholds won't save you when your pipeline silently degrades in production.
Feature Stores: Overhyped or Underused?
Everyone has an opinion on feature stores. Most of them are wrong. Here's when you actually need one.
RAG Evaluation Frameworks: Beyond 'Does It Look Right?'
Vibes-based RAG evaluation is how you ship broken retrieval to production. Here's what a real eval framework looks like.
Treat Your Prompts Like Code. Because They Are.
Prompt management in production isn't a nice-to-have. If you don't version, test, and deploy prompts with the same discipline as code, you're flying blind.
Why Most AI POCs Die Before Production
The demo worked. Stakeholders loved it. And then nothing happened. Here's why — and how to stop the cycle.
Evals Are Your Test Suite Now
Unit tests don't cover AI behavior. If you're shipping models without eval suites, you're shipping blind.