Thanks for sharing. Prompt management is a must have if automate more things, from personal workflows towards product we build.

Definitely give Langfuse a try, it is a charm to setup and use.

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These principles are not new. But their application in the messy reality of production search was a powerful lesson.

I share the full story here:

www.sebastiansigl.com/blog/rebuild...

❌ Common Pitfall: Equating technical excellence with product success.

✅ The Principle: A product mindset is the true compass.

The goal is not the most sophisticated system; it's the most effective system for the user.

❌ Common Pitfall: Rigid functional roles and hand-offs.

✅ The Principle: Blurring lines creates synergy.

Empower your team. Our progress exploded when data scientists could run A/B tests & engineers could explore data.

❌ Common Pitfall: Chasing offline metrics (nDCG, precision).

✅ The Principle: Business impact is the north star.

If an experiment doesn't move a core KPI (engagement, retention), it's not an improvement.

❌ Common Pitfall: Engineering for "correctness" from day one.

✅ The Principle: Velocity unlocks correctness.

A fast, end-to-end feedback loop (from user action to A/B test) is the only path to finding what "correct" actually is.

❌ Common Pitfall: Treating search as just an algo/infra problem.

✅ The Principle: It's a Data & Product problem first.

An architecture that learns fast from user signals beats one that just serves fast.

Indeed, the prompt matters a lot, especially if you prefer a cost efficient model to make it feasible to run on scale.

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Read the full, in-depth playbook on my blog. No fluff, just actionable advice.

www.sebastiansigl.com/blog/llm-jud...

Relying on a biased judge is like flying a plane with a faulty altimeter. You think you're climbing, but you're headed for the ground.

I’ve written a complete guide on how to diagnose and fix these issues, plus build a resilient evaluation system.

4 & 5/ Authority & Moderation Bias

The Judge is easily fooled.

It falls for fake citations ("Harvard study...") and rewards "safe" refusals that users hate. This erodes trust and makes your product useless.

Fix: Use reference-guided evaluation and mandatory human review for refusal cases.

3/ Self-Enhancement Bias (aka Nepotism)

The Judge prefers answers from its own model family (e.g., GPT-4 judging GPT-4).

This makes objective cross-model benchmarking impossible.

Fix: Use a neutral, third-party judge model (e.g., use a Google model to judge OpenAI vs. Anthropic).

2/ Verbosity Bias

The Judge thinks longer = better.

It will reward a 5-paragraph answer over a correct 2-sentence one. This trains your models to be annoying and unhelpful.

Fix: Add "Be concise" and "Penalize verbosity" directly into your judge's rubric.

1/ Positional Bias

The Judge has a favorite: the first option it sees.

If you A/B test prompts and always put A first, you're not measuring quality—you're measuring position.

Fix: Swap the order and run the test again. If the judgment flips, it's invalid. Simple & powerful.

I've written a full guide with code examples and the 4 core principles for writing AI-ready Python tests.
It's the playbook for harnessing AI speed without sacrificing quality.

Read it here: www.sebastiansigl.com/blog/type-sa...

#Python #Testing #AI #SoftwareQuality

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I've written a full guide with code examples and the 4 core principles for writing AI-ready Python tests.
It's the playbook for harnessing AI speed without sacrificing quality.

Read it here: www.sebastiansigl.com/blog/type-sa...

#Python #Testing #AI #SoftwareQuality