AI Engineering Insights
from the Tensorplay Team
Featured Posts
RAG vs Fine-Tuning: How to Choose the Right Approach for Your LLM
One of the most common questions we get from engineering teams is: "Should we fine-tune our model or...
Why Your LLM Prototype Fails in Production (And How to Fix It)
Every week, a startup team demos their new LLM-powered product and it looks brilliant. The model ans...
Recent Posts
The MLOps Foundation CTOs Need for Reliable AI Products
The operating model behind reliable AI MLOps is the set of operating capabilities that lets a team c...
LLM Inference Cost Optimization: A CTO Playbook
Where inference spend really comes from The most durable way to reduce LLM inference cost is to unde...
How to Make a RAG System Reliable in Production
The reliability boundary in RAG A reliable RAG system retrieves authorized, current, relevant eviden...
How CTOs Should Build an LLM Evaluation Framework
The evaluation decision CTOs need to make An LLM evaluation framework converts "does this feel bette...
AI Production Readiness Checklist for CTOs
What production-ready actually means An AI feature is ready for production when your team can measur...
Reducing LLM Inference Costs by 60%: A Case Study
When a B2B SaaS company came to us, their AI features were a success story — too successful. Their m...
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