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.
Prompt Management
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 prompting strategies, and shows how to manage prompts in Langfuse.
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.
| 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 Prompt Management helps you centrally manage, version control, and collaboratively iterate on your prompts.
Collaboratively version and edit prompts via UI, API, or SDKs.
To get started managing prompts in Langfuse, check out our prompt management documentation.
Getting Started
Congratulations! You’ve compleated every module of the Langfuse Academy. This should give you a good overview of common LLMOps pratices to get you started. On the next page, you can get your certificate for completing the academy.
To start building and improving your LLM applications, you can sign up for a free Langfuse account to start building and improving your LLM applications.