--- title: "Prompt Engineering" description: "6 posts about Prompt Engineering, summarised, each linking to the original." --- # Prompt Engineering > 6 posts about Prompt Engineering, summarised, each linking to the original. ## Articles ### [Master your skills: Building skills you can trust](https://yomu.fyi/post/master-your-skills-building-skills-you-can-trust.md) - Company: [Red Hat](https://yomu.fyi/company/red-hat.md) - Author: Dejan Bosanac - Published: Oct 1, 2026 - Type: Explainer Skills in AI agent projects silently misfire when their trigger descriptions are passive or too general, causing the agent to skip using them. The authoring tool skill-creator, from Anthropic’s skills collection, provides an eval loop that generates positive and negative test prompts and iterates on description accuracy using a training and held-out test set to avoid overfitting. These tests are ephemeral, so the open-source framework promptfoo extends them into permanent configuration files that can be committed alongside skills. Promptfoo’s agent SDK providers test through the actual agent runtime and include a skill-used assertion to confirm proper routing, and its YAML-based configuration fits naturally into version control. The final step is wiring promptfoo evals into CI, using GitHub Actions with path filters to run on pull requests that modify skills, catching regressions before they ship and costing roughly $0.10-0.15 per eval. ### [How Deterministic Controls Turn AI Output Into Reliable Prompt Templates](https://yomu.fyi/post/how-deterministic-controls-turn-ai-output-into-reliable-prompt-templat.md) - Company: [Salesforce](https://yomu.fyi/company/salesforce.md) - Author: Scott Nyberg - Published: Sep 30, 2026 - Type: Explainer Salesforce’s Assistive Authoring aimed to reduce prompt-template creation from 25 minutes to under a minute by using Agent Graph. A router node dispatches natural-language requests to specialized sub-agents for creation, refinement, or clarification while state variables carry context. Deterministic controls prevent the LLM from corrupting routing, record identity, or structured output through guarded transitions and a server-side transpose layer. A discovery service normalizes available data fields into an allowlist, and the LLM can only select from validated references. A human-in-the-loop gate ensures the agent never saves or executes, presenting output as an accept-or-reject diff only. ### [Stripe’s Payment Method Factory: Orchestrating agents for repeated, custom integrations](https://yomu.fyi/post/stripe-s-payment-method-factory-orchestrating-agents-for-repeated-cust.md) - Company: [Stripe](https://yomu.fyi/company/stripe.md) - Author: David Dunne - Published: Sep 30, 2026 - Type: Problem & solution Stripe offers over 125 different payment methods globally, each requiring integrations that previously took up to six months because teams started from scratch. They hypothesized that reusable prompts with hardening could reduce effort, and a bake-off showed vanilla Claude Code took 20 days versus 4 days with their prompts. The resulting factory has created three new payment method integrations and migrated ten existing ones to a modern stack, reducing timelines from six months to two to six weeks. The factory also uses code-based orchestration, continuous knowledge loops where agents suggest prompt improvements, and agents testing their own code against sandbox environments. The factory’s approach has since been extended to Stripe’s tax and verification teams for building their own connectors. ### [On the Effectiveness-Fluency Trade-Off in LLM Conditioning: A Systematic Study](https://yomu.fyi/post/on-the-effectiveness-fluency-trade-off-in-llm-conditioning-a-systemati.md) - Company: [Apple](https://yomu.fyi/company/apple.md) - Published: Sep 30, 2026 - Type: Benchmark Controlling the output of Large Language Models (LLMs) is a central challenge for their reliable deployment, yet current conditioning methods are often evaluated narrowly on effectiveness without considering generation quality. This study systematically investigates a range of conditioning methods in both concept injection and removal scenarios, including efficient steering, activation steering, prompting, and supervised fine-tuning. The authors find that efficient steering methods frequently achieve their conditioning goal at a steep cost to fluency, degrading generation quality. They identify a critical previously overlooked interaction: activation steering methods are far less effective on instruction-tuned models than on their base counterparts. Simple prompting and supervised fine-tuning are viable options for concept injection but are not as good at concept removal, while cheaply computed textual metrics highly correlate with costly LLM-as-judge scores, providing practical insights for evaluation. ### [Prompting Claude's long context window](https://yomu.fyi/post/prompting-claude-s-long-context-window.md) - Company: [Anthropic](https://yomu.fyi/company/anthropic.md) - Published: Sep 9, 2026 - Type: Benchmark An evaluation of Claude’s 100,000-token context window tests prompting techniques for recalling specific information from long documents. Researchers built randomized collages from sections of a July 13 government document, using Claude-generated multiple-choice questions and filtering out questions it could not answer from the source passage alone. They compared a baseline with unrelated examples, dynamically selected contextual examples, and two or five examples, each with or without a scratchpad that instructed Claude Instant 1.2 to extract relevant quotes across roughly 70K and 95K token contexts. Scratchpads and contextual examples generally improved performance, with more examples performing better, although recall could decline when the answer passage was at the end. For Claude 2, the best prompting strategy raised performance from 0.939 to 0.961, a reported 36% reduction in errors; reproducible code was released in the Anthropic Cookbook. ### [Building Effective AI Agents](https://yomu.fyi/post/building-effective-ai-agents.md) - Company: [Anthropic](https://yomu.fyi/company/anthropic.md) - Published: Dec 19, 2024 - Type: Explainer Effective agentic systems need not begin with complex frameworks: the post reports that successful implementations commonly use simple, composable patterns and recommends choosing the least complex solution that meets the task's needs. It distinguishes workflows, which follow predefined code paths, from agents, where LLMs dynamically direct their processes and tool use. The proposed foundation is an augmented LLM with retrieval, tools, and memory, potentially integrated through the Model Context Protocol; larger designs include prompt chaining, routing, parallelization, and orchestrator-workers patterns. Frameworks can simplify implementation but may obscure prompts and responses, so direct API use and understanding underlying code are recommended. Tool definitions are treated as an interface requiring testing, clear examples, low formatting overhead, and mistake-resistant parameters; absolute filepaths eliminated observed errors in a coding agent.