Playbooks

Free AI / ChatGPT prompt template library for content ops — copy-ready prompts for briefs, outlines, drafts, SEO packs, and edits. No signup.

A 2026-ready AI SEO workflow: intent mapping, outline freeze, draft assist, on-page packs, internal links, and measurement — without black-hat tricks or fake test claims.

A practical worksheet to compare small/fast model costs per 1K tokens — GPT-5.6 Luna vs Claude Haiku 4.5, inputs, outputs, retries, and when price alone misleads.

How tool schemas, transcripts, and multi-step agent loops inflate token usage — and a practical way to estimate overhead before you ship.

A practical privacy checklist for adopting AI coding assistants: training defaults, retention, secrets, repo scope, and vendor paperwork — without fake security scores.

Estimate Anthropic Batch API spend before you enqueue: discount assumptions, token shapes, failure retries, and a worksheet that stays honest when list prices change.

What to look for in a free prompt formatter that needs no account: local processing, Markdown cleanup, and a checklist before you paste into production.

How to set canonical URLs when you cross-post AI articles to Medium, newsletters, or company blogs — avoid duplicate-content confusion without myths.

Practical ways to estimate and count LLM tokens offline — heuristics, local tokenizers, and browser tools — before you burn API credits.

Treat system prompts like versioned config: changelogs, owners, diffs, rollbacks, and review — so prompt edits do not become invisible production changes.

A practical checklist to evaluate LLM vendor lock-in — prompts, SDKs, evals, data terms, and exit drills — before your roadmap depends on one provider.

IndexNow vs Google sitemap for small sites: what each does, when to use both, and a practical submit workflow for static Cloudflare Pages properties.

Production-oriented prompt patterns for JSON / schema-constrained LLM outputs: contracts, examples, validation loops, and failure modes — no fake benchmarks.

A practical framework for LLM feature unit economics in SaaS — cost per successful action, gross margin impact, pricing guardrails, and when to kill a feature.

A practical playbook for LLM rate limits: detect 429s, exponential backoff with jitter, budgets, queues, and user-facing degradation — without magical retry loops.

Why local tokenizers disagree with vendor usage fields, when the gap is harmless, and how to document the error band for budgeting and CI.

Build a pricing worksheet for OpenRouter versus direct OpenAI (or any gateway vs direct) — list price, routing fees, retries, and operational tradeoffs.

Defensive playbook for prompt injection on customer-facing bots: trust boundaries, tool permissions, input isolation, monitoring, and incident response — no exploit recipes.

Estimate token and dollar cost for RAG features: chunk sizes, top-k, system overhead, retries, and how context windows blow monthly bills.

A practical playbook to shrink LLM prompts — cut noise, structure context, compress examples — while keeping the behaviors you actually need.

Design a retry budget for LLM-backed features — how many retries, when to fail open/closed, and how retries show up in token spend.

A lean AI tool stack for solopreneurs: writing, research, coding, publishing, and cost control — with selection criteria and a no-hype setup path.

What tokens are, how counting differs by model family, why estimators disagree, and how to budget LLM usage without treating vendor docs as mythology.

How to write system prompts for research agents: roles, tool use, citation rules, stop conditions, and anti-hallucination constraints that hold up in production.

A practical playbook for capping LLM spend: token budgets, model routing, caching, retry policy, and monthly forecasting without a finance PhD.

A concrete pipeline for briefs → drafts → edits → publish using LLMs as assistants, with human gates where quality and liability live.

A field checklist for writing prompts that hold up in production — roles, constraints, output schemas, and failure modes — without hype.