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Track 2
Prompt, Context & Token Discipline
Better prompts, cleaner context, lower token waste
Focus 1Verbalized Sampling Prompts for multi‑agent clarity
Focus 2TOON vs JSON: cut tokens, speed prompts
Focus 3CLAUDE.md & System vs User prompts
Track articles (19)
Curated ValidTec articles for this learning track.
- Make Your Testing Agents Communicate Like a Team: Verbalized Sampling Prompts
- How System vs. User Prompts Change the Game in AI-Powered Software Testing
- TOON vs JSON: Cut Tokens, Speed Up Prompts — with Verbalized Sampling, SLMs, and Google’s RAG API
- Context Engineering for Software Testing: A Practical Playbook (Automation → Manual → LLM/SLM → Copilots)
- CLAUDE.md Files — The Missing Manual (Intro, How-To, and Pro Tips)
- Top 6 AI Skills for 2026 That Will Boost Software Testing Engineer Effectiveness
- Prompting Is Now a Core Skill for Software Test Engineers (2026+)
- How Claude Cowork + Claude Code can 2–5× a Software Test Engineer’s effectiveness
- AI Tools Must Build the “Physics” Into the Product, Not Leave It to the User
- Creating Project Memory in Claude Code to Increase Productivity for Software Testing Engineers
- How Claude Code Project Memory and Skills Can 10x a Software Testing Engineer’s Productivity
- How AutoDream Makes Software Test Engineers More Effective Across Claude Chat, Cowork, and Code
- AutoDream vs Project Memory in Claude: Chat, Cowork, and Code
- How Understanding 10 LLM Engineering Concepts Helps Software Testing Engineers in the SDLC Workflow
- Patterns That Help Software Test Engineers Improve Daily Productivity by 5–10x
- Claude Code, Chat, and Cowork: How Loop Engineering Helps Software Testing Engineers Get Better Results
- Loop Engineering for Software Testing Engineers: Building AI Systems That Improve Through Repetition
- 7 Claude Code Prompts That Help Software Testing Engineers Work Like Experienced Engine
- Claude Code, Chat, and Cowork: How Loop Engineering Helps Software Testing Engineers Get Better Results
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