Andy Agent Lab Updates: AI Workflow Guides and Articles

Added coverage of Astra to the professional ChatGPT guide.

Updated model and reasoning guidance for Chat, Work, and Codex, including Astra and GPT-6 Pro. Added guidance for local and cloud Work and website tasks with cloud browser.

How forged, replayed, or misinterpreted inter-agent messages can trigger unauthorized actions, with controls at message admission and tool execution.

How the need for cognitive closure may interrupt the evaluation, feedback, and revision needed to improve an AI-generated answer.

Research and synthesize a public topic with authoritative sources, citations, competing interpretations, evidence gaps, unresolved questions, and a fixed output limit.

Learn how to replace conversational filler, theatrical personas, leading premises, and competing tasks with prompt language that is clear, operational, and testable.

A research-grounded analysis of how role-play may introduce a false safety premise that changes an LLM's interpretation of a restricted request.

Inspect a supplied repository, ZIP, or code sample and generate focused unit tests that match the project’s existing framework and conventions, with the relevant test command and actual execution result.

A false AI answer is not automatically a lie. This article separates human lying, model confabulation, unsupported inference, system failures, and behavioral deception, and maps each failure mode to the controls that address it.

A practical guide to using ChatGPT professionally: choose the right work surface, model, reasoning effort, and context; configure Projects and Personalization; and verify the result.

Review an existing interface before redesign or implementation with prioritized, evidence-linked findings that separate observed issues from heuristics and items requiring testing.

A mechanism-based comparison of 26 human capabilities with observable AI behavior, separating task performance, biological implementation, technical mechanisms, system composition, and attribution limits.

A technical map of how memory, retrieval, multimodal models, planners, tools, solvers, verifiers, and feedback loops turn model-level limitations into bounded system capabilities.

Create, review, redesign, or implement website UI/UX from supplied business goals, user tasks, content, brand material, accessibility requirements, and technical constraints.

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.

Map prompt injection risks across untrusted tool output, retrieved content, schemas, sessions, agent actions, monitoring, OWASP categories, and NIST controls.

Rules for factual AI answers that require cited public sources, authoritative evidence, official documentation, standards, papers, and unsupported-claim handling.

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.

Explain how AI systems interpret emotional signals in text, why that is not emotional understanding, and where authority boundaries must remain.

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.

Audit crawl and indexing signals, titles, descriptions, headings, internal links, canonical URLs, sitemap evidence, and structured-data readiness.

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.

Learn why uploading a file to an AI system does not prove full-file review and how to verify extraction, retrieval, context coverage, and the final output.

Review whether code, configuration, API, fixture, mock, or test changes are covered by tests and protected against regression.

Choose models and place workflows across Chat, Work, Codex, Projects, Skills, Plugins, existing GPTs, Scheduled Tasks, memory, and Library.

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

Choose where a Gemini workflow belongs: Gems, personalization, connected apps, Google AI Studio, Gemini API, Vertex AI, or runtime prompts.

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 working UI can hide defects in AI-generated code. Learn what to verify and how engineering ownership guides correction, acceptance, and maintenance.

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

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

Explain how multi-path reasoning is orchestrated around autoregressive decoding through sampling, search, evaluation, and synthesis.

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.

Use a claim-level workflow to verify AI answers, citations, source support, uncertainty, and missing evidence before publishing or reuse.

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

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

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

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

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

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

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

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

Learn how to use ChatGPT at work: choose a model and reasoning effort, configure Projects, instructions, memory and context, and verify professional outputs.

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

Choose the correct policy for architecture boundaries, implementation review, official-source checks, and engineering quality gates.

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 prompt templates for research, writing, code review, source verification, evidence boundaries, web search, and reusable AI 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 means, how user beliefs and confidence can shift answers, and how to test agreement pressure and correction selectivity.

Learn five AI prompt failure modes and the evidence rules, coverage checks, tool policies, and acceptance tests that make professional outputs reliable.