# AI prompt templates for daily tasks > Source: > Published: 2026-05-30 21:22:06+00:00 Last Tuesday I spent 47 minutes rewriting the same context block for the fifth time that week. Different task, same problem: I was manually re-explaining my codebase structure, my preferred output format, my team's naming conventions, and the specific constraints of the problem — all from scratch, again, into a blank chat window. The LLM is plenty smart. The bottleneck was me, copy-pasting fragments from Notion, tweaking them, forgetting to update one variable, getting garbage output, and starting over. That moment forced me to stop treating prompts like one-off messages and start treating them like software artifacts. What I found after rebuilding my entire daily workflow around structured prompt templates fundamentally changed how fast I ship. Most developers who are serious about AI have a "prompting style" — a loose mental model of how to phrase things. They've read the guides, they use chain-of-thought, they add a system role. But they're still writing fresh prompts for every new task, which means they're compiling from scratch every single time. The failure mode isn't quality — it's variance. Ad-hoc prompts produce inconsistent output not because the model is unreliable but because your *input* is inconsistent. You phrase the context slightly differently on Monday than Friday. You forget to specify the output schema when you're in a hurry. You describe your constraint stack in a different order and the model weights it differently. Prompts that live in your head are unversioned, undocumented code. You wouldn't ship a function you rewrote from memory each deployment. Don't do it with prompts either. A prompt template isn't a fill-in-the-blank string with `{{task}}` slotted in. That's a prompt *stub*. A real template has four layers: **1. Persistent context** — who you are, what system you're operating in, what the model should always know. This doesn't change per invocation. Your stack, your conventions, your role. Write it once and stop repeating yourself. **2. Task frame** — the *type* of work, not the specific work. "Refactor for readability," "draft a technical spec," "debug this trace." This sets the model's mode before you hand it the specifics. **3. Dynamic slots** — the actual variable inputs: the code snippet, the error log, the feature brief. These are the only things that should change between invocations of the same template. **4. Output contract** — explicitly state what you want back: format, length, what to omit. If you don't specify this, the model optimizes for something that *looks* helpful rather than something that *is* useful for your next step in the pipeline. Most people have layer 3. Almost nobody has layers 1, 2, and 4 consistently. That's the gap. Don't build 40 templates. Build five great ones that cover 80% of your actual workflow. Here's the hit list for developers and builders: **Code review template** — persistent context includes your style guide and language; task frame is always "identify issues, don't suggest rewrites unless asked"; output contract is a numbered list with severity tags. This alone cuts review back-and-forth in half. **Bug triage template** — structured to accept a stack trace, a description of expected vs. actual behavior, and relevant env info as discrete slots. Forces you to actually gather that information before you prompt, which is half the debugging work anyway. **Spec drafting template** — persistent context includes your product's user personas and technical constraints; task frame is "write for an engineer who will implement this tomorrow"; output contract specifies sections required (problem, non-goals, success criteria, open questions). No more specs that read like blog posts. **Meeting/thread summary template** — built for async context: paste in a Slack thread or a meeting transcript, get back a decision log and action item list in a consistent format your whole team recognizes. **Research synthesis template** — for when you're reading five sources and need to build a position. Task frame is "identify contradictions, highlight what's uncertain, don't collapse nuance." Output is a structured brief, not a summary paragraph. Before you finalize any prompt template, run it through this checklist. If it fails any check, it's not ready. ``` PROMPT TEMPLATE FITNESS TEST ───────────────────────────────────────────── [ ] Persistent context is stable — if your system changes, you update ONE place [ ] Task frame is named and reusable — you can invoke this template by a recognizable name [ ] Dynamic slots are explicit — you cannot accidentally run this template without filling them [ ] Output contract is machine-readable — the next step in your pipeline can parse this without manual cleanup [ ] You have run it 5+ times — pattern variance is below your tolerance threshold [ ] It fails gracefully — you know what bad output looks like and what input caused it [ ] Version is tracked — you know what changed between v1 and v2 and why ───────────────────────────────────────────── PASS ALL 7 → Ship it. Fewer than 5 → Stub, not template. ``` The last two checks are where teams consistently fail. Templates that aren't versioned drift invisibly. You tweak the wording one Tuesday and two weeks later you can't explain why the output changed. Treat templates like config files: they belong in version control with commit messages. Single templates are useful. Composed template chains are where the leverage compounds. Take a real workflow: you're building a new API endpoint. Template 1 generates a spec draft from your feature brief. Template 2 takes that spec and produces a test plan. Template 3 takes the test plan and generates stub implementations. Each output becomes the dynamic slot input for the next template. The model never loses context because you've structured the handoffs explicitly. This is what people mean when they talk about "AI workflows" — not magic automation, but deliberate sequencing of structured prompts where each step has a defined input schema and output contract that the next step depends on. The failure mode here is schema mismatch: Template 1's output format doesn't cleanly slot into Template 2's expected input. Fixing this is tedious when templates live in different Notion pages, different system prompts, different chat windows. You spend more time translating between your own templates than doing actual work. This is exactly the problem I kept hitting. And it's what I'm building a solution for. When I started thinking seriously about this problem, the issue wasn't that templates were hard to write — it's that they were impossible to *manage* as a system. They lived in too many places, had no shared context layer, couldn't be chained without manual stitching, and had zero version history. AI Handler is built around a single primitive: the **workflow**, which is a versioned, composable chain of prompt templates with explicit input/output schemas. Instead of maintaining a Notion database of prompt snippets you copy from, you define your persistent context once, attach it to any template, and compose templates into pipelines where output schemas match input slots automatically. The model-agnostic layer means the same workflow runs against GPT-4o, Claude, Gemini — without you rewriting the context block each time. You're not locked into one provider's quirks; you're running your templates against whoever produces the best output for that task type right now. The version control layer means every time you edit a template, the previous version is snapshotted. You can diff template versions the same way you diff code. When output quality regresses, you can bisect directly to the template change that caused it. I'm also building in a shared context store — a single place where your "who I am, what I'm building, what my conventions are" block lives, and every template in your workspace inherits from it. Update it once when your stack changes; every downstream template picks it up automatically. This is honest build-in-public territory: the core workflow engine is working, the context store is in active development, the versioning layer is designed but not yet fully shipped. I'm targeting June 2026 for the public launch. If you're the kind of person who got frustrated reading this because you *recognized* your own workflow in the broken parts — that's exactly who I'm building for. **AI Handler is the unified AI workflow tool I am building. Launching June 2026. Email ceo@eternalsix.com for beta access.**