Prompt Builder v2.17
A Prompt Engineering workbench that helps users build a high‑quality prompt from modular components, test it live via the API, and then iterate with an AI coach that critiques the prompt and supports follow‑up discussion.
What this website is (in plain English)
Prompt Builder v2.17 is designed for a practical reality: most “bad outputs” are caused by missing structure or missing information, not by the model being incapable. The website teaches users to write prompts the way experienced practitioners do—by breaking a prompt into repeatable prompt components and then assembling them into a single, coherent prompt they can run immediately.
Author notes (included as-is)
Prompt Building & Coaching Tool
This Prompt Engineering tool allows users to Build Prompts, Analyze Prompts, and then Discuss the Prompt with a Coach who critiques the final result. The coach then engages into a dialogue where users can pose questions and discuss the critique.
1. Allow users to enter the critical components of Prompt Engineering (role, task, context, output format, and constraints) while teaching them to break these components into more granular aspects of the required prompt.
2. Advises users on using advanced prompt engineering techniques such as
-One shot/Few Shot examples,
-Chain of Thought reasoning to break tasks down to and use step by step thinking,
-Decomposition to split tasks into steps and then synthesize final results,
-Self Critique to evaluate and revise against criteria,
-Perspective Switch for analyzing problems from multiple perspective before synthesizing a final result.
Prompt engineering perspective (why the design works)
1) Component-based prompting
Instead of writing one huge prompt from scratch, v2.17 encourages disciplined structure: each prompt pattern is its own component with a robust template. Users insert components one at a time, edit them, and then combine them into a final prompt.
- Reduces ambiguity by isolating intent (what), context (why/for whom), and constraints (how).
- Improves controllability via explicit output formats and boundaries.
- Creates reusability: components can be mixed-and-matched across tasks.
2) Guided fields (dropdowns + “or type…”)
Each component provides dropdowns that act like “starter ideas,” plus small text inputs so users can override with their real values. This mirrors how experts prompt: use known structure, then fill in task-specific details.
- Dropdowns teach what kinds of information matter (audience, success criteria, deliverable).
- Custom inputs keep the tool practical: real tasks always need specific details.
- Templates remove blank-page friction and make outcomes more consistent.
3) Closed-loop iteration with a Coach
The Prompt Coach is the second half of the learning loop. After users build and run a prompt, the coach critiques the prompt itself and suggests concrete improvements (missing constraints, unclear output format, vague tasks, weak success criteria). Users can then ask follow-up questions and iterate.
Result: prompt engineering becomes a repeatable process—draft → test → critique → improve—rather than guess-and-check.
The user journey (step-by-step, v2.17)
The UI is organized into an explicit sequence of steps. The intent is to teach a reliable workflow for building prompts that produce higher-quality, more predictable outputs.
| Step | What the user does | Why it matters for prompt quality |
|---|---|---|
| Step 1 — Core Prompt Components | Enable core patterns (Role, Task, Context, Constraints, Output Format). For each enabled pattern, choose values (dropdown) and/or type custom values. Click Insert Prompt Component to generate a template, then edit the text area to reflect the real task. | Fixes the most common failure mode: underspecified prompts. Users define who the model is, what it should do, the boundaries, and the response structure. |
| Step 2 — Advanced Prompt Components | Optionally add advanced techniques like few-shot examples, stepwise reasoning scaffolds, decomposition, self-critique, and perspective switching—again via Insert Prompt Component + editing. | Improves performance on complex tasks by adding structure for coverage, reasoning, and QA—without reinventing the technique. |
| Step 3 — Build Prompt | Click Build Prompt to combine all enabled components into one final prompt. Use Clear All to reset. | Produces one cohesive prompt with consistent formatting—ready to run, copy, or reuse. |
| Step 4 — Your Prompt | Review the combined prompt and click Copy to paste it elsewhere (ChatGPT, docs, internal tools). | Establishes a single “source of truth” prompt you can version and refine over time. |
| Step 5 — Run Your Prompt (Optional) | Choose a model (for example gpt-4o-mini) and click Run Prompt. Read the output in Run Result. | Validation is essential: users confirm whether their prompt produces the intended structure, tone, and behavior. |
| Step 6 — Prompt Coach | Click Analyze Current Prompt to receive critique and specific improvement suggestions. | Treats prompts like code: structured review focused on reliability and controllability. |
| Step 7 — Ask the Prompt Coach | Ask follow-up questions to refine (“tighten constraints,” “make tone more executive,” “add a JSON schema,” etc.). | Creates a conversational refinement loop that improves the prompt while teaching technique. |
Practical tip: iterate one change at a time—update one component → Build Prompt → Run Prompt → Coach critique → repeat.
Why this tool is valuable (benefits to highlight on a blog)
It turns prompting into a repeatable system
The biggest win is behavioral: users stop “winging it” and start following a system that consistently produces better prompts. The steps enforce structure and make it obvious what to improve when results are weak.
It’s both a tool and a tutor
Users learn by doing: dropdowns teach common patterns, templates reduce friction, and the coach explains what’s missing and why. Over time, users internalize the patterns and become faster even outside the website.