PRACTICAL GUIDES
AI how-to guides
Step-by-step instructions for using AI tools, building reliable workflows and checking the quality of their output.
EDITOR'S SELECTION
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How to connect multiple Google accounts to ChatGPT without mixing work and personal data
ChatGPT now supports multiple Gmail, Google Calendar and Google Contacts accounts. This guide sets up clear account boundaries and verifies every cross-account result.
· Maya Ellison · 10 min readAn AI agent sandbox checklist after the Hugging Face incident
OpenAI's incident shows why a virtual machine is not an isolation strategy by itself. This checklist covers networks, credentials, shared services, stop conditions and response.
· Ethan Brooks · 11 min readHow to evaluate a transcription model that rewrites speech
Gemini 3.5 Transcribe can remove filler words, format text and resolve corrections. Those features require separate tests for readability, fidelity and decision-critical accuracy.
· Priya Raman · 10 min readDesign permissions before a voice assistant can act
Gemini Live's new inbox and long-running task features make a useful design principle urgent: speaking an instruction should not silently grant unlimited authority.
· Maya Ellison · 9 min readHow to migrate GitHub Copilot workflows from models deprecated on September 1
GitHub is retiring six Copilot models on September 1. This guide inventories model dependencies, enables supported alternatives and validates agent workflows before the old selectors disappear.
· Owen Wright · 10 min readAn ONNX portability checklist for moving models between runtimes
ONNX makes model exchange possible, but it cannot guarantee identical behavior in every runtime. Use this validation sequence before treating a converted graph as deployable.
· Elena Morris · 10 min readHow to build a held-out evaluation for speech recognition
A useful ASR test set must resemble production without becoming part of the optimization loop. This guide combines temporal separation, error weighting and audio-level review.
· Priya Raman · 10 min readHow to use AI for data analysis without losing the audit trail
AI can accelerate exploration, formulas and narrative summaries, but analysts still need a reproducible path from source data to every published claim.
· Elena Morris · 10 min readA zero-retention checklist for buying enterprise AI
A vendor's zero-retention claim is the beginning of a privacy review, not the conclusion. Map every feature, processor and log before sensitive data enters the system.
· Priya Raman · 9 min readBuild an AI task-management workflow with approval gates
The useful AI task manager does not automatically act on every request. It turns messy input into a reviewable plan, applies permissions and records what happened.
· Ethan Brooks · 10 min readHow to choose an AI search tool without losing the evidence
AI search can compress research time, but fluent synthesis can hide weak retrieval. Evaluate source coverage, citation fit, freshness and reproducibility before trusting an answer.
· Elena Morris · 9 min readA reliable AI presentation workflow from source to slides
AI can draft a deck quickly, but a useful presentation still needs a clear argument, verified numbers, visual hierarchy and an accountable final editor.
· Maya Ellison · 8 min readHow to audit content for AI search visibility
Google says conventional SEO fundamentals still apply to AI features. A useful audit now also records the global participation control and dedicated Search Console visibility signals.
· Maya Ellison · 10 min readContext windows are capacity limits, not memory guarantees
A large context window tells you how much a model can accept, not whether it will use every detail accurately. Test retrieval, instruction stability, latency and cost together.
· Elena Morris · 9 min readWhy AI produces confident false answers—and how to verify them
NIST calls the problem confabulation: confidently stated erroneous content. Understanding why it happens leads to better interfaces, tests and review practices.
· Elena Morris · 9 min readAn AI music production checklist for rights and release
Generating a track is only the first step. Producers need records for prompts, source audio, collaborators, likeness, distribution terms and meaningful human authorship.
· Priya Raman · 8 min readA practical AI code-review workflow for pull requests
AI review can find patterns and draft fixes, but it should complement tests, security controls and accountable human approval rather than become an automatic merge signal.
· Ethan Brooks · 9 min readHow speech-to-text AI works—and how to test it
Modern transcription systems are impressive, but word error rate alone cannot tell you whether they will work for meetings, interviews or regulated records.
· Priya Raman · 10 min readAI meeting notes need a privacy and accuracy plan
Automatic notes can save hours, but recording, transcription and summarization create different risks. This workflow keeps participants informed and decisions verifiable.
· Maya Ellison · 8 min readHow to automate an AI workflow without losing human control
Reliable automation begins with a bounded task, explicit permissions and measurable approval gates—not an agent with unrestricted access.
· Ethan Brooks · 10 min readHow to evaluate an AI model API before you commit
A leaderboard cannot tell you which model belongs in your product. Measure quality, latency, cost, safety and operational fit together.
· Elena Morris · 10 min readHow to evaluate an AI coding assistant across a real team
Autocomplete demos miss the work that determines value: review load, model portability, security, repository context and whether generated changes survive production.
· Ethan Brooks · 10 min readHow graphic designers can use AI without losing the system
The useful design workflow is not prompt-to-publish. It combines rapid exploration with brand constraints, editable assets, provenance and a deliberate human finish.
· Noah Chen · 9 min readNatural language processing, from tokens to modern AI systems
NLP spans classifiers, search, embeddings and large language models. This guide explains the core ideas and practical choices without treating every text problem as a chatbot problem.
· Elena Morris · 10 min readHow to use ChatGPT for a resume without sounding generated
A strong AI-assisted resume starts with evidence, not adjectives. This workflow uses ChatGPT to organize achievements and tailor language while keeping every claim accurate, specific and recognizably yours.
· Maya Ellison · 10 min readPrompt engineering in 2026: a reliable workflow, not a bag of magic phrases
Good prompting makes the task, evidence and acceptance criteria explicit. The durable skill is designing a repeatable evaluation loop that survives model changes—not collecting incantations.
· Maya Ellison · 11 min readHow does AI work? From training data to predictions, generation and feedback
Modern AI systems learn statistical relationships from examples, turn new inputs into outputs and improve through evaluation and feedback. The important details lie in the objective, the data and the surrounding controls.
· Priya Raman · 12 min readHow to evaluate a multimodal model
Models that read text, images, audio and video promise one interface for many kinds of work. A useful comparison tests perception, reasoning, latency and failure behavior separately.
· Priya Raman · 9 min readHow to follow the public trail of AI training data
No single disclosure explains a foundation model. Model cards, dataset papers, licenses, copyright filings and filtering notes can still reveal what a developer has documented—and what remains unknown.
· Priya Raman · 10 min read