Langfuse Prompt Migration

Migrate hardcoded prompts to Langfuse for version control, A/B testing, and deployment-free iteration.

Prerequisites

Verify credentials before starting:

echo $LANGFUSE_PUBLIC_KEY # pk-... echo $LANGFUSE_SECRET_KEY # sk-... echo $LANGFUSE_HOST # https://cloud.langfuse.com or self-hosted

If not set, ask user to configure them first.

Migration Flow

1. Scan codebase for prompts
2. Analyze templating compatibility
3. Propose structure (names, subprompts, variables)
4. User approves
5. Create prompts in Langfuse
6. Refactor code to use get_prompt()
7. Link prompts to traces (if tracing enabled)
8. Verify application works

Step 1: Find Prompts

Search for these patterns:

Framework Look for

OpenAI messages=[{"role": "system", "content": "..."}]

Anthropic system="..."

LangChain ChatPromptTemplate , SystemMessage

Vercel AI system: "..." , prompt: "..."

Raw Multi-line strings near LLM calls

Step 2: Check Templating Compatibility

CRITICAL: Langfuse only supports simple {{variable}} substitution. No conditionals, loops, or filters.

Template Feature Langfuse Native Action

{{variable}}

✅ Direct migration

{var} / ${var}

⚠️ Convert to {{var}}

{% if %} / {% for %}

❌ Move logic to code

{{ var | filter }}

❌ Apply filter in code

Decision Tree

Contains {% if %}, {% for %}, or filters? ├─ No → Direct migration └─ Yes → Choose: ├─ Option A (RECOMMENDED): Move logic to code, pass pre-computed values └─ Option B: Store raw template, compile client-side with Jinja2 └─ ⚠️ Loses: Playground preview, UI experiments

Simplifying Complex Templates

Conditionals → Pre-compute in code:

Instead of {% if user.is_premium %}...{% endif %} in prompt

Use {{tier_message}} and compute value in code before compile()

Loops → Pre-format in code:

Instead of {% for tool in tools %}...{% endfor %} in prompt

Use {{tools_list}} and format the list in code before compile()

For external templating details, fetch: https://langfuse.com/faq/all/using-external-templating-libraries

Step 3: Propose Structure

Naming Conventions

Rule Example Bad

Lowercase, hyphenated chat-assistant

ChatAssistant_v2

Feature-based document-summarizer

prompt1

Hierarchical for related support/triage

supportTriage

Prefix subprompts with _

_base-personality

shared-personality

Identify Subprompts

Extract when:

• Same text in 2+ prompts

• Represents distinct component (personality, safety rules, format)

• Would need to change together

Variable Extraction

Make Variable Keep Hardcoded

User-specific ({{user_name}} ) Output format instructions

Dynamic content ({{context}} ) Safety guardrails

Per-request ({{query}} ) Persona/personality

Environment-specific ({{company_name}} ) Static examples

Step 4: Present Plan to User

Format:

Found N prompts across M files:

src/chat.py:

• System prompt (47 lines) → 'chat-assistant'

src/support/triage.py:

• Triage prompt (34 lines) → 'support/triage' ⚠️ Contains {% if %} - will simplify

Subprompts to extract:

• '_base-personality' - used by: chat-assistant, support/triage

Variables to add:

• {{user_name}} - hardcoded in 2 prompts

Proceed?

Step 5: Create Prompts in Langfuse

Use langfuse.create_prompt() with:

• name : Your chosen name

• prompt : Template text (or message array for chat type)

• type : "text" or "chat"

• labels : ["production"] (they're already live)

• config : Optional model settings

Labeling strategy:

• production → All migrated prompts

• staging → Add later for testing

• latest → Auto-applied by Langfuse

For full API: fetch https://langfuse.com/docs/prompts/get-started

Step 6: Refactor Code

Replace hardcoded prompts with:

prompt = langfuse.get_prompt("name", label="production") messages = prompt.compile(var1=value1, var2=value2)

Key points:

• Always use label="production" (not latest ) for stability

• Call .compile() to substitute variables

• For chat prompts, result is message array ready for API

For SDK examples (Python/JS/TS): fetch https://langfuse.com/docs/prompts/get-started

Step 7: Link Prompts to Traces

If codebase uses Langfuse tracing, link prompts so you can see which version produced each response.

Detect Existing Tracing

Look for:

• @observe() decorators

• langfuse.trace() calls

• from langfuse.openai import openai (instrumented client)

Link Methods

Setup How to Link

@observe() decorator langfuse_context.update_current_observation(prompt=prompt)

Manual tracing trace.generation(prompt=prompt, ...)

OpenAI integration openai.chat.completions.create(..., langfuse_prompt=prompt)

Verify in UI

• Go to Traces → select a trace

• Click on Generation

• Check Prompt field shows name and version

For tracing details: fetch https://langfuse.com/docs/prompts/get-started#link-with-langfuse-tracing

Step 8: Verify Migration

Checklist

• All prompts created with production label

• Code fetches with label="production"

• Variables compile without errors

• Subprompts resolve correctly

• Application behavior unchanged

• Generations show linked prompt in UI (if tracing)

Common Issues

Issue Solution

PromptNotFoundError

Check name spelling

Variables not replaced Use {{var}} not {var} , call .compile()

Subprompt not resolved Must exist with same label

Old prompt cached Restart app

Out of Scope

• Prompt engineering (writing better prompts)

• Evaluation setup

• A/B testing workflow

• Non-LLM string templates