Module 5: Prompt Management / Engineering
After evaluating your LLM workflows, you’ll often find areas to improve—whether by refining prompts, changing models, or updating tool definitions. In this module, we’ll look at how to apply those findings through systematic prompt management and iterative experimentation.
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Prompt Management
Effective prompt management keeps your LLM application agile, reproducible, and collaboration‑friendly. Without a systematic way to store, version, and experiment with prompts, seemingly minor text tweaks can break customer flows or silently inflate costs. This module shows why prompt management matters, introduces core prompting strategies, and walks through Langfuse’s prompt store and experiment features.
Why Prompt Management?
- Reproducibility & rollback – Prompts evolve faster than code; versioning prevents silent regressions and enables instant rollback when quality dips.
- Governance & auditability – Regulated domains (health, finance, legal) must trace which exact wording produced an output .
- Collaboration across teams – Product managers and domain experts often iterate on prompts; a central prompt store avoids “prompt spaghetti” in codebases.
- A/B testing & optimisation – Structured experiments reveal cost/quality trade‑offs and prevent prompt drift.
- Common pitfalls → brittle hard‑coded strings, shadow prompts living in notebooks, unclear ownership, and uncontrolled temperature/parameter changes.
Introduction to Common Prompting Strategies
If you are new to prompting, here is a rough overview of different strategies that can improve the performance of your application. For more advanced prompting strategies, we collected some high-quality resources here.
| Strategy | Core Idea | When to Use | Key Risk |
|---|---|---|---|
| Zero‑Shot | Provide only task instructions; rely on model generality | Fast prototyping | Ambiguous outputs |
| Few‑Shot / In‑Context | Add 1‑5 examples to steer style or structure | Structured outputs, data‑sparse tasks | Higher token cost |
| Chain‑of‑Thought (CoT) | Ask model to reason step‑by‑step before final answer | Complex reasoning tasks | Latency, leak chain to user |
| Role Prompting | Assign the model a persona or professional role | Tone control, empathy | Over‑constrained style |
| Retrieval‑Augmented Generation (RAG) | Dynamically inject retrieved docs into context | Fresh, source‑grounded answers | Retrieval latency |
| Prefix‑Tuning / System‑Content Split | Separate stable system message from dynamic user message | Multi‑turn chat apps | Duplication across turns |
Using Prompt Management in Langfuse
Langfuse offers a Prompt Store where prompts live as first‑class versioned entities; each version links to the traces it produced for instant cost/quality analysis. You can:
- Create & edit prompts via UI, API, or SDK without redeploying the app.
- Pin versions to environments (e.g.,
prodvsstaging) to avoid accidental cross‑contamination. - Run A/B experiments by splitting traffic across prompt versions and comparing metrics directly in Langfuse dashboards.
- Link prompts to evaluations so that score regressions surface next to the exact text diff .
To get started managing prompts in Langfuse, check out our prompt management documentation.
Prompt Engineering Loop
For most LLM applications, it is important to involve domain experts in the design of LLM prompts.
- Define success criteria – domain stakeholders translate policy/compliance or UX goals into measurable metrics (accuracy, tone, latency).
- Draft baseline prompt – engineer assembles initial system + user prompt following chosen strategy.
- Share in Langfuse Prompt Experiments – non‑technical reviewers comment, annotate token costs, and suggest edits in the UI (no Git access needed) .
- Run controlled experiment – split traffic 80/20 between baseline and candidate; Langfuse auto‑collects costs, eval scores, and feedback .
- Review & decide – cross‑functional meeting reviews dashboards; if candidate wins on KPIs, promote to
prod. - Post‑mortem & document – every prompt update auto‑links to traces and eval runs, building an audit trail .