Advanced Prompt Engineering: Roles, Constraints and Evaluation
A LangGPT reference for structured prompts, roles, goals, rules, workflows, output formats, prompt chains and repeatable evaluation.
What this archive covers
LangGPT organizes complex prompts into roles, background, goals, rules, skills, workflows, examples and output formats. The archive also discusses meta-prompts, prompt chains and reusable examples. The structure is a maintenance aid, not a universal template that guarantees a result across every model.
Start with a minimum contract
For one task, keep only the goal, available input, hard constraints, expected output and a test example. Add modules when they solve a real ambiguity. A structured prompt should make its assumptions and acceptance conditions easier to inspect, not merely add headings.
Evaluate changes as experiments
Prepare normal, boundary and missing-information cases. Change one module at a time and record the model version, inputs, outputs, factual accuracy, format stability and human-editing cost. A meta-prompt still needs evaluation on the actual task it is meant to improve.
Maintenance note
Different models interpret long context, labels and formatting differently. Keep templates versioned and remove secrets or private data from examples. Content review date: 2026-08-23.
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LangGPT prompt engineering study guide
Build a small task contract first, test it against normal and boundary cases, then add only the prompt modules that improve a measurable outcome.
Before you start
- Prepare a Markdown reader and a model session suitable for non-sensitive experiments.
- Choose a small task with a clear pass or fail condition.
- Prepare normal, boundary and missing-information test inputs.
Quick start
- 01
Understand the modules
Identify what roles, background, goals, rules, skills, workflows, examples and output formats each contribute before copying a full template.
- 02
Build the smallest template
Keep the goal, input, constraints and output format for one task, then add a verifiable example that represents the expected result.
- 03
Design evaluation cases
Use normal, boundary and incomplete inputs, and record where the structured prompt succeeds or fails.
- 04
Version the iteration
Change one module at a time and preserve the reason, test inputs and outputs so improvements remain traceable.
Usage tips
- More modules do not automatically improve a prompt; structure should reduce ambiguity or maintenance cost.
- Prompt chains need explicit input and acceptance conditions at every step.
- Test templates across the model versions and context lengths that will actually be used.
Troubleshooting and uninstall
Why does the structured prompt sound mechanical?
Remove unnecessary role and formatting rules, restore the task audience and useful examples, and allow natural organization within the hard constraints.
What if two rules conflict?
Place hard factual, safety and format constraints clearly, remove duplicate or contradictory clauses and use boundary tests to confirm priority.
Frequently asked questions
Which modules are common in LangGPT prompts?
Roles, background, goals, rules, skills, workflows, examples and output formats are common modules, but a task rarely needs every module.
Are structured prompts always better than short prompts?
No. A clear short prompt is often best for a simple task; structure helps when requirements are complex, shared or repeatedly tested.
What is a meta-prompt?
A meta-prompt analyzes, generates or improves another prompt. Its output still needs evaluation on a real task and boundary examples.
How can prompt improvements be measured?
Hold the model, inputs and evaluation criteria steady, then compare factual accuracy, constraint adherence, format stability and human editing effort.