#130 — The founder's guide to evals

AI evals are systematic tests and grading methods designed to assess the performance, reliability, and real-world usefulness of AI outputs. Founders need them to catch serious flaws before launch, track progress over time, and avoid costly mistakes. For example, early evals with LLM-based graders helped Jasper AI eliminate 70% of hallucinated facts before hitting scale—saving weeks of negative user feedback and rapid churn.

You don't need pricey vendor subscriptions. Open-source frameworks like Phoenix and Ragas let founders automate LLM-based grading, human feedback collection, and code-based tests for free. For instance, a health startup used Ragas to create custom evals for misleading advice, catching subtle tone errors that saved them from a failed product launch.

Use human evaluators for high-stakes tasks—like medical, legal, or financial advice—where trust and accuracy matter the most. LLM-based evaluators work best at scale for everyday tasks, letting you grade thousands of outputs per week. Case study: An edtech platform combined human graders for curriculum content and LLM evaluators for student Q&A, cutting costs and improving accuracy.

Yes. By designing evals specifically for hallucination and toxicity detection, founders can catch these issues early. Real world: A travel booking bot used evals to flag when trips included destinations not in the user's search, preventing hundreds of booking errors and angry calls.

Start by defining what 'good' looks like for your use case—hallucination, accuracy, tone, safety. Draft prompt templates and logic to match your product’s domain. Example: For a legal AI assistant, founders coded tests to check for reference citations and LLM graders to review argument structure.

Treat eval design as a collaborative, iterative process. Lean on open-source guides, prompt libraries, and real user feedback to improve. A fintech startup ran weekly eval workshops with cross-functional input and raised output quality by 35% by the third sprint.

Weekly or with every major LLM/algorithm update. Continuous eval cycles catch drift, regressions, and new failure modes. Case in point: A customer service chatbot with monthly eval reviews reduced bad escalation events by nearly half after tracking hallucination rates over time.

Mistakes include only testing happy paths, ignoring hallucination/toxicity, or assuming human-label quality is enough. Avoid them by stress-testing edge cases and combining automated and manual grading. Example: Early-stage startups often missed retrieval errors until systematic evals flagged unsupported answers.

Absolutely. Ship with at least one automated and one human eval, build feedback prompts into your product, and benchmark against public datasets. Real world: A SaaS PM tool added a user-facing feedback button and used it to tune LLM grading prompts, boosting accuracy by 20% in two releases.

Robust evals mean fewer angry users, higher retention, and better reviews. Founders who prioritize evals often see smoother launches and faster iteration cycles. Case study: A social media AI used evals to catch hateful content before it reached users, protecting the brand and growing daily active users by 10%.

AI evals measure qualitative attributes like reasoning, user satisfaction, and context awareness, not just deterministic pass/fail logic. For instance, where unit tests check if a function returns a correct value, evals can grade if an AI agent books the right flight, answers with the correct information, or maintains an appropriate tone—even with variable responses. This helps founders catch issues traditional tests miss, especially with generative and LLM-based products.

Relying only on prompt engineering leaves major blind spots—prompts direct behavior, but without evals, you can’t measure effectiveness, detect hallucinations, or guarantee safety. Real-world example: Product teams at leading AI companies found that even with great prompts, user trust eroded when agents occasionally made up facts or acted unpredictably. Evals provided the safety net and insights they needed.

Start by identifying critical product outcomes—such as no hallucinations, high factual accuracy, or non-toxic tone. Use common eval frameworks like Phoenix and Ragas for standardized benchmarks (e.g., retrieval accuracy, summarization quality). For example, a legal AI tool set a benchmark that every referenced statute must match the ground truth from provided case law documents, graded both by LLMs and human experts.

LLM-based evals are powerful for automating large-scale grading, but they work best when combined with occasional human review for calibration and trust. Production systems often use confidence scores or judge panels to reduce errors. Case study: An enterprise SaaS team used three judge LLMs per output, releasing only outputs with unanimous high-confidence labels, minimizing risk.

Common mistakes include only testing for happy paths, using vague thumbs-up/down metrics, or skipping checks for hallucinations and toxic content. For instance, early travel bots frequently missed destination errors until founders added specific, robust evals for city and flight accuracy metrics.

Open-source tools provide ready-made, domain-tested evaluation templates for hallucinations, toxicity, retrieval, and correctness. They lower the barrier for startups to implement mature eval stacks and avoid vendor lock-in. A startup used Phoenix to track hallucination rates and, after optimization, reduced user-reported errors by 30% in three weeks.

Regularly revisit your evals with each major release, adding tests for new user flows and failure cases, and incorporating real customer feedback. Growing products, like EdTech AIs, expanded evals from simple answer accuracy to tone, inclusivity, and varying difficulty, keeping up with new use cases as their audience scaled.

Continuous evals ensure you catch regressions and edge cases before users do, preventing viral failures and costly brand damage. For example, an AI project management tool’s regular eval cycles caught a bias issue early, allowing for a proactive fix before a major publicity launch.

Use human-in-the-loop evals for mission-critical or highly subjective products (e.g., medical or legal), and automated LLM/judge-driven evals where scale and speed are required. Many teams start with human evals to calibrate prompts and then transition to automated systems for ongoing monitoring, as seen with financial chatbots.

Map out each step of the user journey and define evals for every key interaction. State-of-the-art teams use panel LLM judges, cross-comparison scoring, and failure mode analysis to spot rare but critical issues. For example, a multi-agent AI assistant startup designed evals for data retrieval, synthesis quality, context retention, and safe handoff between agents, achieving higher reliability and differentiated user experience.