Practical thinking for teams building with AI
Notes on LLM integration, RAG, and agents — and the product decisions that keep these systems working in production.
PageTruth Is Now Open Source: Answers That Open the Exact Page
PageTruth is a team knowledge base that answers only from your documents and cites the exact page. We've open-sourced it under MIT. Here's how it works, what our evals found, and how to run your own.
From the team.
- · AI Engineering · 3 min read
The Wiki That Never Goes Stale: Let Your Coding Agent Own the Docs
Project knowledge is scattered and every wiki drifts. Our proposal: a small markdown wiki inside the repo that your coding agent reads before each change and updates after it — with a sample repo to clone.
- · AI Engineering · 3 min read
8 Ways to Cut Your Token Usage in Claude Code
Claude Code bills by the token, and almost all of that cost is context you keep re-sending. Here are eight habits that shrink the bill without slowing you down.
- · AI Development · 6 min read
Your Evals Are the Product, Not the Prompt
The prompt is the easy, visible part. The eval suite is the thing you're really building — and the artifact that quietly becomes the most valuable thing you own.
- · AI Development · 5 min read
Fine-Tuning vs RAG: Choosing the Right Approach for Your Business
Fine-tuning bakes knowledge into the model. RAG keeps it in your data layer. Here's how to choose, with a short decision checklist at the end.
- · AI Engineering · 6 min read
RAG: The Antidote to AI Hallucinations in Enterprise Systems
AI hallucinations are the number one barrier to enterprise adoption. Learn how Retrieval-Augmented Generation (RAG) grounds your AI in verified data, reducing hallucinations from 40% to under 5%.
- · AI Development · 6 min read
From Prototypes to Production: The Reality of Deploying AI Agents
The gap between a controlled proof-of-concept and a production-ready AI agent is massive. Learn how to build resilient, fault-tolerant autonomous systems.
- · AI Strategy · 7 min read
Why Hiring an AI Consulting Firm Beats Building an Internal AI Team
Building an internal AI team costs $500K+ per year in salaries alone. Here's why partnering with an AI consulting firm delivers better results at a fraction of the cost.
- · AI Development · 8 min read
How AI-Accelerated Development Cuts Your Time-to-Market by 80%
AI-powered development workflows are transforming how software gets built. Learn how companies are shipping features in days instead of months.
- · LLM Integration · 10 min read
The Complete Guide to LLM Integration for Business Applications
Adding large language model capabilities to your existing apps doesn't have to be complex. Here's a practical guide to LLM integration that actually works.
- · RAG · 9 min read
RAG Solutions: How to Make AI Actually Understand Your Business Data
Generic AI doesn't know your business. RAG (Retrieval-Augmented Generation) bridges that gap by grounding AI responses in your actual data. Here's how it works.
- · AI Strategy · 6 min read
5 Signs Your Company Needs an AI Development Partner
Not sure if you need external AI help? These five signals indicate it's time to bring in an expert partner instead of going it alone.
- · AI Development · 8 min read
From POC to Production: Scaling AI Solutions Without Internal AI Engineers
Your AI proof-of-concept works. Now what? Here's how to take AI from demo to production-grade without hiring a single AI engineer.
- · RAG · 7 min read
How RAG Reduces AI Hallucinations and Builds Trust in Enterprise AI
AI hallucinations are the #1 barrier to enterprise AI adoption. RAG is the most effective solution — here's why and how to implement it correctly.
- · AI Training · 6 min read
Why Training Your Staff on AI Tools Is the Best ROI Investment in 2025
AI tools are only as good as the people using them. Companies that invest in AI training see 3-5x productivity gains. Here's how to do it right.
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