How do we build an evaluation suite if we do not have labeled test data?

Start with 50-100 hand-curated examples per prompt. Product managers and domain experts can create these in a few hours by writing the inputs they expect users to send and the outputs they consider acceptable. For generative prompts where outputs vary, use rubric-based evaluation (is the output factual? is it concise? does it address the query?) rather than exact match.

Should prompts be in code or in a separate system?

Separate systems (prompt management platforms, dedicated Git repos) offer better collaboration, versioning, and evaluation integration. Prompts in application code work for small teams with few AI features. The tipping point is usually around five production prompts. Beyond that, finding and managing prompts scattered across the codebase becomes error-prone.

How do we handle model migrations (e.g., switching from one LLM to another)?

Treat a model migration as a prompt-wide evaluation event. Run every prompt against its evaluation suite on the new model before switching. Prompts that pass can migrate immediately. Prompts that fail need retuning for the new model's behavior. Budget 1-2 weeks for retuning work when planning model migrations.

What is a good eval pass rate target?

It depends on the stakes. Customer-facing prompts that generate responses users trust should target 90%+ pass rates. Internal analytics prompts where a human reviews the output can accept 80%. The key is that the target is set before optimization, not rationalized after the fact. Track this alongside the [AI task success rate](/metrics/ai-task-success-rate) for end-to-end quality measurement. ---

Prompt Engineering Roadmap Template

Quick Answer (TL;DR)

This free PowerPoint template plans prompt engineering work across four tracks: Prompt Library, Testing & Evaluation, Version Control & Governance, and Optimization. Each track has initiative cards with measurable quality targets and cost impact estimates. Download the .pptx, inventory your current prompts, and build a roadmap that moves prompt engineering from ad-hoc string editing to a disciplined, measurable practice with clear ownership and quality gates.

What This Template Includes

Cover slide. Product name, number of AI features using prompts, and the PM or ML lead responsible for prompt quality.
Instructions slide. How to catalog existing prompts, set evaluation baselines, and implement version control. Remove before presenting.
Blank prompt engineering roadmap slide. Four tracks (Prompt Library, Testing & Evaluation, Version Control, Optimization) with initiative cards on a quarterly timeline. Each card shows the affected feature, quality metric target, and cost implication.
Filled example slide. A SaaS product's prompt engineering roadmap showing centralized prompt repository migration, automated eval suite for customer support prompts, Git-based prompt versioning, and chain-of-thought optimization that cut token cost per interaction by 40%.

Why Prompt Engineering Needs a Roadmap

In most organizations, prompts are treated like configuration strings. Edited in place, tested manually, and owned by whoever wrote them last. This works when one engineer manages one AI feature. It falls apart when ten features depend on prompts written by different people at different times, with no shared standards, no evaluation baselines, and no way to tell whether a prompt change improved or degraded quality.

Prompt engineering is a development discipline, not a one-time task. Prompts degrade when models are updated (a prompt tuned for GPT-4 may behave differently on GPT-4o). Prompts interact with each other in multi-step chains where one change cascades. Prompts have direct cost implications. A verbose prompt that adds 500 tokens per call at 10M calls/month is a material line item.

The prompt engineering for PMs guide covers techniques and best practices. This template turns those practices into a sequenced plan with deadlines, owners, and measurable outcomes.

Template Structure

Four Engineering Tracks

Columns represent the prompt engineering capability areas:

Prompt Library. Centralizing all production prompts in a shared repository with metadata: feature name, model provider, creation date, author, last evaluation date, and performance baseline. This replaces scattered prompts in code, config files, and database records.
Testing & Evaluation. Building automated evaluation suites that run on every prompt change. Each prompt gets a test set of inputs and expected outputs. The eval pass rate metric measures whether a prompt change improves or degrades quality. Evaluation runs before any prompt reaches production.
Version Control & Governance. Git-based prompt versioning with branching, pull reviews, and change history. Governance rules define who can modify production prompts, what review process is required, and how rollbacks work. This prevents unauthorized or untested prompt changes from affecting users.
Optimization. Reducing cost and latency without sacrificing quality. Techniques include prompt compression, chain-of-thought refinement, few-shot example pruning, and model routing (sending simple queries to cheaper models). Track prompt-to-value ratio to measure output quality relative to cost.

Initiative Cards

Each card contains:

Initiative name. Specific work item (e.g., "Build eval suite for document summarization prompts").
Affected feature. Which AI feature this prompt work supports.
Quality target. Measurable outcome (e.g., "Eval pass rate > 92% on 200-case test set").
Cost impact. Expected change in token cost per interaction after optimization.
Owner. Engineer or PM responsible for delivery.

Quality Dashboard Strip

A bottom strip shows the aggregate prompt health across the product: total prompts in library, percentage with evaluation coverage, average eval pass rate, and monthly prompt-related inference cost. This gives leadership a single view of prompt engineering maturity.

How to Use This Template

1. Inventory all production prompts

Find every prompt in your codebase, configuration systems, and databases. Most teams discover prompts they forgot existed. A prompt for an edge case feature written six months ago by someone who left the company. Document each prompt's purpose, owning feature, and current model target.

2. Establish evaluation baselines

For each prompt, create a baseline evaluation: a set of test inputs and expected outputs that define acceptable quality. Run the current prompt against this set and record the pass rate. This baseline is essential. Without it, you cannot tell whether future changes improve or break the prompt.

3. Migrate to a centralized library

Move all prompts into a shared repository with consistent metadata. This does not require specialized tooling initially. A Git repository with structured directories per feature works. The key is that every production prompt has a single source of truth with ownership and last-evaluated dates.

4. Implement evaluation-gated deployments

No prompt change ships to production without passing its evaluation suite. Integrate evaluation runs into your CI/CD pipeline or deployment workflow. A prompt that passes evaluation can deploy automatically. A prompt that fails evaluation triggers a review. The LLM evaluation framework provides structured approaches for building these evaluation suites.

5. Optimize for cost and latency

Once evaluation coverage is in place, start optimizing. Shorten system prompts that include unnecessary context. Reduce few-shot examples from ten to three if evaluation scores hold. Route simple queries to smaller, cheaper models. Each optimization should be measured against the evaluation suite to verify quality is preserved.

When to Use This Template

A prompt engineering roadmap is the right format when:

Five or more AI features depend on prompts and no centralized management exists
Prompt quality is inconsistent across features, with some well-tuned and others untouched since initial development
Model provider updates (new API versions, model swaps) create risk of prompt degradation
Token costs are growing and optimization requires a structured effort across multiple features
Multiple engineers modify prompts with no review process or change tracking

For a single AI feature's full development lifecycle, the AI feature roadmap template covers the broader scope. For the ML infrastructure that supports prompt serving and evaluation, the AI ops roadmap template addresses the platform layer.

Featured in

This template is featured in AI and Machine Learning Roadmap Templates, a curated collection of roadmap templates for this use case.

Key Takeaways

Prompt engineering spans four tracks: Prompt Library, Testing & Evaluation, Version Control, and Optimization.
Every production prompt needs a centralized home with ownership, metadata, and an evaluation baseline.
Evaluation-gated deployments prevent untested prompt changes from reaching users.
Prompt optimization (compression, few-shot pruning, model routing) reduces cost without sacrificing quality when measured against evaluation suites.
Aggregate prompt health metrics give leadership visibility into prompt engineering maturity and cost trends.
Compatible with Google Slides, Keynote, and LibreOffice Impress. Upload the .pptx to Google Drive to edit collaboratively in your browser.

Prompt Engineering Roadmap Template for PowerPoint