PRACTICAL AI GUIDES
Find practical AI guides for your task.
Practical guides to choosing models, working with agents, and reducing token usage.
Try hands-on prompt and context lessons ↗20 guides
Prompt caching, explained
Learn what is reused and check cache usage.
Keep context useful, not endless
Trim repeated history and oversized tool output.
Use the right model for each step
Route routine work and define clear upgrade rules.
Ask for the output you actually need
Reduce extra generation and parsing failures.
Set a budget for an agent task
Control retries and tool output, then verify.
How to read model benchmarks
Understand common AI benchmarks, score types, test settings, and the limits of a leaderboard rank.
Coding plans vs. API billing
Choose a billing approach by checking compatibility, usage limits, and your actual workload.
Switch agents and keep the settings that matter
An agent migration guide to preserving preferences, project rules, memory, Skills, MCP connections, and automations—with a settings worksheet and checks for what actually transferred.
Before switching AI assistants, write your personal brief
Carry your working preferences and active projects into a new assistant with a reviewable brief, a project card, and a small acceptance check.
Move AI memories into Claude—and check what survived
Find the correct import flow, audit a small set of memories, test behavior in a fresh conversation, and repair omissions without assuming a complete transfer.
Start with Muse: hand over your habits and project context
Give Meta's personal agent a clear first task, a reviewed background brief, and explicit boundaries before adding connections or recurring work.
An agent keeps interrupting you: adjust its notifications
Trace repetitive alerts to their tasks, separate check frequency from notification rules, and verify that a quieter agent still reports what matters.
Your agent remembers it wrong: fix stale facts and conflicting instructions
Trace an incorrect answer to its source, make a scoped correction, and verify the result across new conversations and recurring tasks.
Compare agent workflows: time, corrections, and cost
Use a complete fictional inbox task, fixed acceptance criteria, and a blank results sheet to measure useful outcomes without inventing a leaderboard.
Jev vs Laya vs Kev: choose a decision model
Compare API access, local deployment and probability outputs, then build a shortlist around your task.
Route an agent request with a decision model
Define clear intent labels, keep uncertain cases visible and validate the selected tool before it runs.
Check RAG evidence before generating an answer
Evaluate relevance and evidence coverage after retrieval, and measure what filtering removes.
When do decision models actually save money?
Measure complete-task cost, fallback frequency and accepted quality before claiming token savings.
FAQ RAG: fixed windows or paragraph chunks?
A 14-question pilot found equal strict answer scores, with 13.0% fewer input tokens for paragraph chunks. Inspect the failures, dataset and runnable experiment.
Repeated document QA: does prompt order reduce cost?
In a 24-request pilot, document-first prompts reused 12,800 input tokens. See initial versus later requests, estimated costs, answer failures and the reproducible data.