Updates and release notes

A sink-aware security model for validating, authorizing, encoding, and testing LLM output before it reaches browsers, databases, shells, code runtimes, tools, or privileged workflows.

How competing system objectives can produce acceptable outputs without completing the user’s actual task.

A technically precise explanation of four commonly conflated concepts: the language model, retrieval-and-generation pattern, goal-directed execution system, and integration protocol.

A user prompt can be one part of the information available to an AI model. Learn what model context can include, what may remain outside it, and why context is not the same as a context window or instruction authority.

The guide now covers the current ChatGPT reasoning-level picker, Search, Deep research, Agent mode, personalization, Memory, Temporary Chat, Library, and Projects.

A vendor-agnostic systems model for working state, persistent memory, functional memory roles, representation forms, storage and access substrates, retrieval, updates, and evaluation.

Use a structured draft, claim extraction, verification-question, independent-checking, and final-revision workflow before treating an AI answer as final.

Use the Claude map to choose between Projects, Skills, Claude Code, MCP, subagents, artifacts, and runtime prompts.

Use the Gemini map to choose between Gems, Connected Apps, Google AI Studio, Gemini API, File Search, Vertex AI, and related tool-use surfaces.

Use source rules, tool requirements, stop conditions, output structure, and validation checks to make professional AI work more consistent.

Use it to compare existing tests against supplied features, behavior, requirements, implementation evidence, assertions, fixtures, and coverage material.

Use it to check direct and adjacent behavior that may break after a supplied code, API, configuration, schema, dependency, or integration change.

Use it to protect private messages, attachments, tool access, backend workflows, and agent permissions before connecting AI systems to Gmail or WhatsApp data.

Architecture-level security analysis of prompt injection as a boundary-control failure across instruction, context, schema, session, retrieval, output, agency, and monitoring boundaries.

Rules for factual answers backed by cited public sources such as standards, papers, and official documentation.

Use this prompt component to keep Hebrew responses readable when they mix RTL Hebrew with LTR English, code, URLs, paths, filenames, commands, numbers, or identifiers.

Explains how AI assistants interpret emotional signals in text, why that is not emotional understanding, and why affective inference must not control authorization, truth, or tool use.

Step-by-step workflow for answering public factual questions with authoritative sources, stable citations, and fail-closed handling when evidence is missing.

Technical guide for turning important factual claims into verification questions before writing the final answer.

Reusable AI prompt components for evidence boundaries, verification checks, source review, artifact reading, confidence scoring, and no-fabrication controls.

Use this prompt to request web search for current, changing, or hard-to-verify public information, with cited sources and fail-closed behavior when evidence is insufficient.

Use this evidence mode to require authoritative citations, academic register, structured evidence labels, fail-closed behavior, and confidence scoring.

Use this prompt to review supplied code, configuration, or design materials against official documentation, standards, or primary vendor guidance.

Use this prompt component when answers must rely only on files, logs, screenshots, excerpts, repository snapshots, ZIPs, or other material supplied in the current request.

How private-message content from Gmail and WhatsApp Business can cross into tool selection, external actions, persistent memory, and data disclosure in AI-agent workflows.

Use this prompt to review system architecture, layering, dependency direction, interface boundaries, state ownership, and minimal structural remediation from supplied code or design materials.

Review software layers, module boundaries, ownership, dependency direction, interfaces, and coupling using evidence from inspected files and diagrams.

Use this prompt to review whether GA4 events and conversion-funnel tracking make important user actions, subscription paths, checkout steps, and conversion outcomes visible.

Use this prompt to review whether a page or site can be discovered, crawled, indexed, understood, and internally connected before or after publishing.

Use this guide to review whether a page or site is discoverable in search and measurable through GA4 events, subscription tracking, checkout tracking, and conversion-funnel visibility.

Use this prompt component to add an evidence-based confidence score to standard AI responses.

Procedure for adding an evidence-based confidence score to non-sentinel answers while preserving fail-closed evidence behavior.

A technical explainer on why file upload is not proof of full-file review in AI workflows, how file content may be extracted, split, retrieved, and selected, and how professional workflows check source coverage before trusting the output.

Use this guide to review whether code, configuration, API, fixture, mock, or test changes are adequately covered by tests and protected against regression.

Use this reference to decide where a workflow belongs in ChatGPT: Projects, GPTs, Skills, memory, tools, apps, agent mode, or runtime prompts.

Use this reference to decide where a workflow belongs in Claude: Projects, Skills, Claude Code, MCP, subagents, artifacts, or runtime prompts.

Use this reference to decide where a workflow belongs in Gemini: Gems, personalization, connected apps, Google AI Studio, Gemini API, or Vertex AI.

Use this reference to decide when an AI workflow belongs in an API, internal agent, backend service, retrieval system, tool workflow, or production automation.

Reusable AI prompt components for evidence boundaries, verification checks, source review, artifact reading, confidence scoring, and no-fabrication controls.

Use this prompt to review grammar, spelling, punctuation, clarity, sentence flow, terminology consistency, and readability while preserving the original claims and meaning.

Use this prompt to review an academic or research article draft for claim support, citation coverage, argument structure, academic register, and unsupported overclaims.

Use this prompt to turn a technical goal into minimal, understandable code while checking missing context, avoiding automatic agreement, and preventing invented project details.

Use this prompt to plan a small behavior-preserving code refactor with explicit preserved behavior, risk areas, required checks, and test impact.

Use this prompt to review code quality, readability, maintainability, naming, modularity, error handling, dependency use, testability, and alignment with relevant language or framework guidance.

Use this prompt to identify missing test scenarios, weak assertions, setup gaps, and regression-sensitive paths after a code or configuration change.

A professional analysis of vibe coding, AI-assisted software development, and the engineering risk created when generated code is accepted without understanding, review, testing, and maintainability controls.

Step-by-step AI task guides for source rules, output review, code review, prompt setup, context boundaries, memory boundaries, and publishing measurement.

Client-side black-box analysis of observed ChatGPT classification artifacts across user access, prompt demand, and capability allocation.

See how parallel reasoning is orchestrated around sequential autoregressive decoding through sampling, search, evaluation, and synthesis, plus key scope exceptions.

Rules for when to use web search, how to select public sources, and how to cite current or niche factual claims.

Step-by-step guide for requesting web browsing for current or niche public questions, with citation-grade outputs and fail-closed behavior.

Choose whether an AI answer should rely on uploaded files, cited public sources, or academic-style evidence review before you use it.

Why LLM fluency does not guarantee factual accuracy, and how retrieval, provenance, claim-level validation, and fail-closed controls improve reliability.

Step-by-step workflow to verify claims, citations, source support, and uncertainty in AI-generated answers before publishing or reuse.

Step-by-step AI task guides for source rules, output review, code review, prompt setup, context boundaries, memory boundaries, and publishing measurement.

Step-by-step AI task guides for source rules, output review, code review, prompt setup, context boundaries, memory boundaries, and publishing measurement.

Browse copy-ready AI prompt templates for research, writing, code review, source verification, analysis, and reusable workflow controls.

Browse copy-ready AI prompt templates for research, writing, code review, source verification, analysis, and reusable workflow controls.

Browse copy-ready AI prompt templates for research, writing, code review, source verification, analysis, and reusable workflow controls.

Browse copy-ready AI prompt templates for research, writing, code review, source verification, analysis, and reusable workflow controls.

Browse copy-ready AI prompt templates for research, writing, code review, source verification, analysis, and reusable workflow controls.

Use this prompt to summarize files, notes, logs, reports, threads, or pasted excerpts without adding unsupported claims.

Define a professional task, choose the right ChatGPT experience and capability, place context correctly, and verify the result before use.

Threat model for web retrieval prompt injection in LLM systems, covering external content, tool routing, downstream actions, and control boundaries.

Routing page for choosing the correct review policy for architecture, boundaries, or implementation against official sources.

Decision guide for choosing the right code review method for architecture, implementation, testing, code quality, or refactoring.

Reusable AI prompt components for evidence boundaries, verification checks, source review, artifact reading, confidence scoring, and no-fabrication controls.

Browse copy-ready AI prompt templates for research, writing, code review, source verification, analysis, and reusable workflow controls.

Why clowns, AI-generated faces, and fluent AI text can feel uncanny when human-like cues conflict with emotion, realism, or factual coherence.

Comparison of LLM-led and orchestrator-led tool execution in LLM systems, including control-plane placement, reliability, observability, latency, cost governance, and security.

Learn what LLM sycophancy is, how preference signals can reward agreement over correction, and how paired-prompt evaluations can detect regressions.

Fix unsupported answers, partial input coverage, unchecked agreement, missing tools, and vague output requirements with explicit prompt controls and tests.