Observed Signal · Jul 28, 2026 · Technical Guide · Source: Aakash Gupta · Impact: 2/5 · Sentiment: Positive
Build Your First AI Eval in Claude
This guide (published 2026-07-28) explains a practical method for building an offline, "cold-start" evaluation (eval) for AI features before any production data exists. Drawing on Daniel McKinnon (former PM on Llama at Meta), the article describes creating an eval project in Claude or ChatGPT, defining a one-sentence feature spec, constructing an answer-first set of cases (floor and ceiling), using AI to generate ~100 varied test cases, and grading outputs with a calibrated binary pass/fail judge across three criteria (substantive, format, scope). It recommends iterative slicing to set guardrails and targets for engineering, highlights that PM judgment is central to eval design, and includes sponsors and related resources and podcasts.
Provides a practical, reproducible method (cold-start offline evals) for PMs and AI teams to validate LLM features before launch; useful for AI product development but not industry-shifting.
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Key Takeaways & Evidence Grounding
- Article published on 2026-07-28 on Aakash G's newsletter site (news.aakashg.com).
- Presents a step-by-step method to build a "cold-start" offline eval for an AI feature using Claude or ChatGPT before production data exists.
- Method components include: define a one-sentence feature spec, create a floor (easy) and ceiling (hard) case, generate ~100 intermediate cases with AI, and grade outputs using a binary pass/fail judge with three criteria (substantive, format, scope).
- Daniel McKinnon, a PM who worked on the Llama models at Meta, contributed the approach and previously wrote the piece "Show, Don’t Tell."
- Sponsors mentioned in the article include SerpApi, Product Faculty, Ariso, Land PM Job, and Pendo.
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Advanced Evals Guide: Finding Hidden AI Failures
This article is a comprehensive guide on advanced evaluation (evals) practices for AI products, authored by Hamel Husain and Shreya Shankar. It emphasizes the importance of error discovery before writing metrics, contrasting it with product discovery. The authors outline a three-step process for effective error discovery using coding agents like Codex or Claude, highlighting the pitfalls of automation bias and criteria drift. They introduce an open-source 'evals skills' plugin that assists in trace review and clustering. The article cites real-world examples from companies like Shopify, Cursor, Ramp, and Harvey, demonstrating how evals have led to significant product improvements. The piece also discusses synthetic data generation and the importance of human-in-the-loop annotation, recommending a target of 100 traces for meaningful analysis.
Guide to Claude /goal for Reliable AI Agents
A technical guide published on May 15, 2026 by Linas on Substack explains how Anthropic’s Claude Code /goal mechanism turns a session into an autonomous loop that runs and verifies a goal condition until completion. The piece covers how /goal evaluates conditions, a three-element formula for writing evaluable conditions, reliability architecture for multi-hour agent runs, and three production-grade prompt templates tailored to fintech workflows (competitive research, code-heavy builds, and continuous portfolio/market monitoring). The guide stresses that long-run agent reliability depends on the harness and engineering practices (context management, model selection, data sensitivity tiers, environment segregation, regulatory output flagging), and links to companion posts covering Claude usage limits and Claude Code routines.
Build a Self‑Improving Claude Code AI Knowledge System
This technical guide explains how to build a self-improving AI knowledge system using Claude Code and Cowork. The author describes a file-based knowledge graph architecture (CLAUDE.md as the brain, indexed knowledge folders, and progressive disclosure) that ingests data, organizes knowledge, runs hypothesis tracking, and compounds improvements over time. The system was tested on social content (X/Twitter) and evolved through iterative phases: raw import, knowledge hierarchy, and automation with scripts and agents. The post details practical components (templates, hypothesis logs, false-belief catalogs), cross-surface workflows across Claude Code, Cowork and web, and notes temporary doubled usage limits for Claude as an opportunity to start building.
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