# Yaron Helfer

> Product leader, AI adoption practitioner, speaker and mentor.

This personal site focuses on product and AI mentoring. Published essays disclose AI assistance; the homepage metric charts are fictional illustrations, not client outcomes. Use each article's canonical page and original publication date when citing it.

## Start here
- [Yaron Helfer | Product & AI Mentoring for Growth](https://yaronhelfer.com/): Product and AI mentoring for PMs, founders, small businesses and solo entrepreneurs. Book a call to clarify your next move.
- [About Yaron Helfer](https://yaronhelfer.com/about/): Product leader, former developer, speaker and mentor focused on making AI useful.
- [Product mentorship for your next chapter](https://yaronhelfer.com/mentoring/): Practical guidance for product managers developing judgment, influence, AI fluency, and a professional voice.
- [Speaking and mentoring](https://yaronhelfer.com/speaking/): Talk topics and product mentoring with Yaron Helfer: AI adoption, knowledge as a product, and autonomous SDLC.
- [Editorial policy](https://yaronhelfer.com/editorial/): How sources, AI assistance, corrections and updates are handled.

## Published articles
- [LLMOps: turn production traces into release evidence.](https://yaronhelfer.com/writing/llmops-production-traces-to-evals/): Observability becomes more valuable when real failures feed a curated evaluation set, a clear quality bar and a deliberate release decision.
- [ASDLC: the harness is part of the product.](https://yaronhelfer.com/writing/asdlc-harness-engineering/): As coding agents take on longer tasks, product leaders need acceptance contracts, controlled autonomy and evidence of useful delivery—not just more generated code.
- [AI governance is a product design problem.](https://yaronhelfer.com/writing/ai-governance-product-design/): Turn permissions and oversight into understandable product behavior, rather than a policy users never see.
- [The dark factory still needs a product mind.](https://yaronhelfer.com/writing/dark-factory-product-leadership/): Autonomous delivery makes intent, acceptance criteria, and accountability more valuable—not less.
- [A company brain needs a memory policy.](https://yaronhelfer.com/writing/company-brain-knowledge-product/): Shared AI context is only useful when ownership, freshness, and access are part of the architecture.
- [When tokens become a product metric, count the outcome.](https://yaronhelfer.com/writing/ai-product-value-metrics/): Connect AI resource use to successful tasks, quality, and the effort required to recover from failure.
- [From smart AI to real usage: design the next step.](https://yaronhelfer.com/writing/smart-ai-real-usage/): The path from an impressive answer to repeated use runs through trust, guidance, and a clear next action.
- [Knowledge Base as a Product: the missing layer of AI adoption.](https://yaronhelfer.com/writing/knowledge-base-as-a-product/): Give organizational knowledge users, ownership, quality standards, and a roadmap.

## Questions and answers
- [FAQ library](https://yaronhelfer.com/faq/): Browse answers and guides by category.
- [How can I promote my business with AI?](https://yaronhelfer.com/faq/how-to-promote-my-business-with-ai/): Use AI to support one specific marketing task, such as drafting an offer, adapting approved content, or comparing campaign ideas. Keep your audience,.
- [What is a dark factory in software development?](https://yaronhelfer.com/terms/dark-factory/): A dark factory is a metaphor for highly automated software delivery in which agents implement work from specifications and feedback. It is not a.
- [What is a company brain for AI?](https://yaronhelfer.com/terms/company-brain/): A company brain is an informal name for a connected knowledge foundation that helps people and AI find relevant organizational context. It needs more.
- [What is LLMOps and when do I need it?](https://yaronhelfer.com/terms/llmops/): LLMOps is the operational practice of developing, evaluating, deploying, and maintaining applications built around large language models. It becomes.
- [What can an SRE agent do safely?](https://yaronhelfer.com/tech/sre-agent/): An SRE agent can assist incident investigation by gathering operational evidence, proposing hypotheses, and suggesting remediation. Begin with.
- [What is LiteLLM used for?](https://yaronhelfer.com/tech/litellm/): LiteLLM provides interfaces and a proxy layer for working with language-model providers. A gateway can centralize aspects of request handling,.
- [What is an AI agent, and when is one useful?](https://yaronhelfer.com/terms/ai-agents/): An AI agent uses a model to choose actions and tools while pursuing a task. It is useful when the path cannot be fully prescribed in advance. A fixed.
- [What is context engineering for AI agents?](https://yaronhelfer.com/terms/context-engineering/): Context engineering is the work of selecting and organizing the information an AI system receives for a task. It includes instructions, retrieved.
- [What is an agent harness?](https://yaronhelfer.com/terms/agent-harnesses/): An agent harness is the surrounding system that runs a model-driven workflow: it handles tools, state, execution limits, feedback, and recovery. The.
- [What are agent evaluations?](https://yaronhelfer.com/terms/agent-evaluations/): Agent evaluations test whether a model-driven system completes representative tasks within acceptable behavioral boundaries. They can examine the.
- [What is spec-driven development with AI?](https://yaronhelfer.com/terms/spec-driven-development/): Spec-driven development makes an explicit description of intended behavior a working input to implementation. With AI coding tools, the specification.
- [What are agentic workflows in the SDLC?](https://yaronhelfer.com/terms/agentic-workflows/): Agentic workflows combine ordinary software automation with model-driven judgment for tasks such as issue triage, documentation, or change.
- [What is Model Context Protocol, or MCP?](https://yaronhelfer.com/tech/model-context-protocol/): Model Context Protocol is a standard interface for connecting AI applications with external capabilities and context. It can make integrations more.
- [What does the Claude Agent SDK provide?](https://yaronhelfer.com/tech/claude-agent-sdk/): The Claude Agent SDK provides building blocks for running Claude-based agents with tools and context-management capabilities. It is useful when you.
- [How can I automate small-business workflows with AI?](https://yaronhelfer.com/faq/how-to-automate-small-business-workflows-with-ai/): Start with a repetitive workflow that has clear inputs, a recognizable result, and an inexpensive way to check mistakes. Use AI for the part that.
- [How can product managers use AI for customer research?](https://yaronhelfer.com/faq/how-to-use-ai-for-customer-research/): AI can help organize interview notes, propose themes, and draft follow-up questions, but it cannot replace contact with customers or turn generated.
- [How do I evaluate an AI agent before launch?](https://yaronhelfer.com/faq/how-to-evaluate-an-ai-agent/): Evaluate an AI agent against the intended workflow, not just a polished demonstration. Check task completion, forbidden actions, recovery, latency,.
- [How can I build an MVP with AI?](https://yaronhelfer.com/faq/how-to-build-an-mvp-with-ai/): Use AI to reduce the effort of testing a specific product assumption, not to build every feature in the imagined product. A useful MVP connects one.
- [What is AI governance for a small product team?](https://yaronhelfer.com/terms/ai-governance/): AI governance is the set of responsibilities, decisions, and controls that define how an AI system may be used and changed. A small team can start.
- [Where should a human stay in the loop for AI agents?](https://yaronhelfer.com/faq/where-should-a-human-stay-in-the-loop/): Put human intervention where a person can change a consequential decision before it becomes difficult to reverse. Useful checkpoints include.
- [Should I use RAG or fine-tuning for my AI product?](https://yaronhelfer.com/faq/rag-or-fine-tuning/): Use retrieval-augmented generation when the system needs relevant external knowledge at response time. Consider fine-tuning when you need to adapt.
- [How should I measure the ROI of an AI feature?](https://yaronhelfer.com/faq/how-to-measure-ai-roi/): Measure AI ROI by comparing the value of a completed workflow with its full incremental cost. Define a baseline, a measurement period, and the.
- [What prompt helps an AI coding agent clarify a specification?](https://yaronhelfer.com/faq/prompt-for-ai-specifications/): A useful specification prompt asks the assistant to expose uncertainty before it writes code. Include the user problem, intended behavior,.
- [How can I practice product sense with AI?](https://yaronhelfer.com/faq/how-to-practice-product-sense-with-ai/): Use AI as a practice partner that asks follow-up questions and critiques your reasoning against a consistent rubric. State your user, problem,.

## Optional
- [Published content export](https://yaronhelfer.com/llms-full.txt): Full text of the published essays, with sources and disclosures.
- [Sitemap](https://yaronhelfer.com/sitemap.xml): Canonical public page discovery.