Drop the anchor
Evaluation-Driven Development is the anchor in a sea of non-deterministic LLM calls. This guide shows you how to build one, eval by eval, from scratch.
/THE FREE GUIDE · 26 PAGES · 2026
From guesswork to confidence with evals.
A field guide to Evaluation-Driven Development for AI applications — the missing test layer for non-deterministic systems.
- Build datasets that actually catch regressions before your users do.
- Pick the right evaluator — and know when LLM-as-Judge is the wrong call.
- Ship self-correcting systems using online evals in production.
/SHIPPING AI IN PRODUCTION WITH
/WHY READ THIS
Stop shipping AI on vibes.
LLMs are non-deterministic. Every prompt tweak, model swap, and RAG config change is a coin flip — unless you have evals. This guide is the playbook we use internally to ship AI agents you can trust.
Get past the demo phase
Without robust evals, AI work becomes a game of guesswork and whack-a-mole that rarely gets past the demo. Here's what the teams that ship do differently.
Make informed tradeoffs
Cost vs. latency vs. quality — make those calls with data, not gut feel. Evals give you the numbers to defend every decision in a deploy review.
/WHERE THIS PLAYBOOK CAME FROM
We wrote it from real engagements.
Every idea in the guide has been battle-tested on production AI work we've shipped for Focused clients. Four engagements that shaped the approach:
AI in Real Estate
We worked alongside Hamlet's team to build smarter, more scalable systems using LangChain, LangGraph, and LangSmith.
Insurance Underwriting
Delivered a new end-to-end underwriting workflow for enhanced risk assessment capabilities.
Hertz Rental
We built three custom mobile apps to help Hertz recover stolen inventory and expand to new markets.
Wayfair
We created a sophisticated API-first supplier experience to drive revenue.
/INSIDE THE GUIDE
Six chapters. Twenty-six pages. Zero fluff.
- 01 Understanding the Evals Landscape What evals actually are, the LangSmith vocabulary you need, and how to talk about quality with a non-technical stakeholder without hand-waving.
- 02 Building Your Foundation — Datasets How to create, organize, and grow evaluation datasets that catch the regressions your unit tests never could.
- 03 Choosing and Implementing Evaluators Custom code, LLM-as-Judge, and composite evaluators — when to reach for each, and when each one will lie to you.
- 04 Evaluation Strategies by App Type RAG, agents, multi-step workflows, and conversational AI each need different evals. Here's what works for each.
- 05 Best Practices & Common Pitfalls What teams who've shipped this stuff know — and the traps that tripped them up getting there.
- 06 Advanced Techniques Annotation queues, online evals, automations, and self-correcting systems that use evals inline during real production traffic.
/WHO IT'S FOR
Built for the people actually shipping.
AI engineers
Tired of eyeballing outputs. You want a real test layer so you can refactor a prompt on a Friday without a pit in your stomach.
Eng leaders
Whose teams can't tell whether the last prompt tweak helped or hurt — and can't defend roadmap calls with anything other than "it feels better."
Product folks
Making cost / latency / quality tradeoffs in meetings where the numbers don't exist yet. Evals give you the numbers.
/GET THE GUIDE
Where should we send it?
We'll email the PDF instantly. You'll also get an option to download it right here on the next page.
/ABOUT FOCUSED
Consultants who build AI agents that work.
Building AI is easy — building agents that integrate with the systems enterprises actually run is hard. From LangChain to LangGraph to LangSmith, Focused builds AI agents that integrate into existing systems to automate human processes. This guide is the playbook we use internally on client engagements.
Visit focused.io →/QUESTIONS
The usual questions.
Is it really free?
Yes. We'd rather give the playbook away and have the right teams know who we are. No gate beyond your email.
Will I get spammed?
No. You'll get the guide, maybe one or two follow-ups with related engineering resources, and that's it. Unsubscribe is one click.
Do I need to use LangSmith to benefit?
The guide uses LangSmith for the concrete examples because it's the platform we use on real engagements, but the principles apply to any AI development workflow. The chapters on datasets, evaluators, and eval strategies are framework-agnostic.
Who wrote it?
The engineering team at Focused Labs — the same people shipping AI agents into production for DoorDash, Wayfair, Hertz, Panasonic and others. No ghostwriters, no content agency.
Can my whole team get a copy?
Absolutely — forward it along, or send them this link. The more people on your team thinking about evals, the better your AI ships.