Writing from the studio.
Practical notes on AI automation — what works in production, what doesn’t, and how we think about the decisions in between.
AI Tools We Actually Use in Production (2026)
Forget the hype. We share the essential AI tools professional engineers at Dainty use daily for evals, tracing, prompting, and deployment in 2026.
Why “SEO” Now Means Being Cited by ChatGPT Too
Generative engine optimization (GEO) is the practice of structuring a site so AI answer engines like ChatGPT, Claude, and Perplexity can find, understand, and cite it accurately.
What an SEO & GEO Audit Actually Finds
The recurring, fixable issues we find on almost every unaudited site: broken schema, missing llms.txt, robots.txt accidentally blocking AI crawlers, and thin metadata.
What a Production AI Project Actually Costs
A production AI project typically costs $15k to over $200k, driven by complexity, evaluation needs, and robustness requirements.
Can AI Automate Your Process? Ask These 4 Questions.
Before investing in AI automation, ask four critical questions: Is the input consistent? Is the logic describable? Is the output verifiable? Is the volume worth it?
How long does it take to build a production AI agent?
A reliable production AI agent takes 2–3 months to build. The demo takes a week, but the gap is evaluation pipelines, fallback handling, and edge cases.
The unglamorous ops work behind production AI
What happens after the AI demo: managing P99 latency, sanitizing PII from prompt logs, and handling malformed JSON failures in production.
Stop Hitting LLM Rate Limits: What We Learned Shipping
Learn production patterns for rate limiting AI endpoints, including per-user, cost-based, and queue-based throttling, to avoid provider limits and unexpected bills.
Don't Ship AI Features to 100% on Day One
Gradual rollouts, A/B testing, and shadow mode are critical for AI features. Learn how to instrument and deploy safely.
How We Evaluate LLMs: Beyond Benchmarks
Stop guessing which LLM works best. Our framework helps you pick the right model for your task, measuring cost, quality, and long-term fit.
Self-hosting LiteLLM: 6 months in production
After half a year, we share what actually works when self-hosting LiteLLM as a unified LLM gateway, and where it adds complexity.
Rules Still Win: When Not to Use an LLM
LLMs aren't a silver bullet. We break down Dainty's decision tree for when deterministic rules outperform AI models.
Building AI Feedback Loops That Don't Require Manual Labeling
Stop waiting for user ratings. Learn how to build a reliable AI evaluation framework using automated checks, sampling, and implicit signals.
Token Cost Optimization: Where the Savings Actually Are
The highest ROI strategies for reducing LLM token costs in production: prompt caching, model routing, context trimming, and output constraints.
How to Add AI to an Existing SaaS Without Rewriting It
Most SaaS products don’t need a rebuild to get AI features. They need one well-chosen workflow, a clean API endpoint, and a prompt that doesn’t hallucinate on your data.
What an AI Automation Sprint Actually Looks Like
A fixed-scope, fixed-price, four-week engagement. Here’s what happens each week, what you get at the end, and what we won’t scope in.
LLM Routing: Why We Run Claude, Gemini, and OpenAI Behind One Gateway
Hard-coding a single model provider into your app is a liability. Here’s how we route across models — and the rules we use to decide which one runs what task.
The AI Features That Actually Show Up in Your P&L
Most AI features don’t move the needle. A few do — reliably and measurably. Here’s how to tell the difference before you build.
Webhook vs Polling for AI Integrations: When Each Makes Sense
Both patterns work. The right choice depends on latency requirements, whether the data source emits events, and how much you want to think about retry logic at 3am.