LangChain Prompt Engineering Framework

A modular, extensible framework for advanced prompt engineering, evaluation, and optimization—built entirely on official LangChain APIs.

---

Why Use This Framework?

Prompt engineering for LLMs is a rapidly evolving field. Ad-hoc scripts and notebooks quickly become unmanageable as you scale up:

• Templates get duplicated and lost
• Prompt evaluation is inconsistent
• Switching LLM providers is painful
• No easy way to optimize or test prompts at scale

This framework solves these problems by providing:

• Reusable, composable utilities for prompt management, LLM selection, evaluation, and optimization
• A robust CLI for experimentation, batch testing, and automation
• A clear separation between reusable logic and demos/scripts
• Easy integration into pipelines, UIs, or production systems

---

Who Is This For?

• Prompt engineers and LLM application developers who want to move beyond notebooks and ad-hoc scripts
• Teams standardizing prompt workflows across projects
• Researchers running systematic prompt evaluations and ablations
• Anyone building LLM-powered products who wants maintainable, testable, and extensible prompt engineering infrastructure

---

Key Features & Advantages

• Official LangChain APIs only: No vendor lock-in, maximum compatibility
• Multi-provider LLM support: OpenAI, Anthropic, HuggingFace, and more
• Prompt template registry: Centralized, versionable prompt management
• Built-in evaluation: Use LangChain's evaluators for single or batch scoring
• Parameter sweep optimization: Systematic prompt/LLM parameter tuning
• CLI and (optional) UI: Use interactively, in scripts, or as a backend
• Extensible: Add your own templates, evaluators, or UI

Compared to ad-hoc approaches:

• No more copy-pasting prompt code
• Consistent, testable workflows
• Easy to scale from experimentation to production

---

Project Structure

langchain-prompt-utils/
│
├── langchain_utils/              # Reusable package
│   ├── __init__.py
│   ├── prompt_management.py      # Template registry & management
│   ├── llm_provider.py           # Multi-provider LLM selection
│   ├── evaluator.py              # Evaluation utilities
│   ├── optimizer.py              # Parameter sweep optimization
│   ├── cli.py                    # CLI entry point
│   └── ui.py                     # (optional) Web UI
│
├── examples/                     # Usage demos and workflow scripts
│   └── workflow_integration.py
│
├── tests/                        # Unit/integration tests
│
├── docs/                         # Documentation
│   ├── genai-workflow-integration.md
│   ├── cli_usage.md
│   └── ...
│
├── templates/                    # Prompt templates (JSON)
│
├── .env.example
├── .gitignore
├── LICENSE
├── pyproject.toml
└── README.md

---

Installation

pip install -e .

Set your API keys in .env or as environment variables:

export OPENAI_API_KEY=your-openai-key
export ANTHROPIC_API_KEY=your-anthropic-key

---

CLI Usage

The CLI provides a robust interface for prompt engineering workflows.

List Templates

python -m langchain_utils.cli list-templates

Generate Output

From a registered template:

python -m langchain_utils.cli generate --template summarization --vars '{"text": "LangChain is a framework for LLM apps."}'

From a raw template string:

python -m langchain_utils.cli generate --template "Translate to French: {text}" --vars '{"text": "Hello"}' --raw

Evaluate Output

python -m langchain_utils.cli evaluate --template summarization --vars '{"text": "LangChain is a framework for LLM apps."}'

Batch Evaluation

Prepare a test cases file (see docs/cli_usage.md for format):

python -m langchain_utils.cli batch-evaluate-cmd --template summarization --test-cases tests/summarization_tests.json

Parameter Sweep Optimization

python -m langchain_utils.cli optimize --template summarization --test-cases tests/summarization_tests.json --param-grid '{"temperature": [0.3, 0.7], "max_tokens": [128, 256]}'

See docs/cli_usage.md for full details and troubleshooting.

---

Programmatic Usage

Use the framework as a library in your own pipelines:

from langchain_utils import PromptRegistry, get_llm, evaluate_response

# Load or register templates
registry = PromptRegistry("templates")
prompt = registry.get_template("summarization")

# Get an LLM (OpenAI, Anthropic, HuggingFace, etc.)
llm = get_llm("gpt-3.5-turbo")

# Run a chain
from langchain.chains.llm import LLMChain
chain = LLMChain(llm=llm, prompt=prompt)
result = chain.run(text="Some input")

# Evaluate the result
score = evaluate_response(llm, "Some input", result)

---

Template Management

• Templates are stored as JSON in the templates/ directory.
• Register new templates programmatically or by adding files.
• Use the CLI or API to list, retrieve, or use templates.

Example template file:

{
  "template": "Summarize the following text: {text}",
  "input_variables": ["text"]
}

---

Extending the Framework

• Add new LLM providers: Extend llm_provider.py
• Custom evaluators: Add to evaluator.py
• Web UI: Use langchain_utils/ui.py (Streamlit/FastAPI example included)
• Integrate with LangServe, LangSmith, etc.: See docs/genai-workflow-integration.md

---

Example: Web UI (Streamlit)

# langchain_utils/ui.py
import streamlit as st
from langchain_utils import PromptRegistry, get_llm

st.title("Prompt Engineering Playground")
prompt_text = st.text_area("Prompt Template", "Summarize the following: {text}")
user_input = st.text_area("Input Text", "")
if st.button("Generate"):
    registry = PromptRegistry()
    registry.register_template("custom", {"template": prompt_text, "input_variables": ["text"]})
    prompt = registry.get_template("custom")
    llm = get_llm("gpt-3.5-turbo")
    from langchain.chains.llm import LLMChain
    chain = LLMChain(llm=llm, prompt=prompt)
    result = chain.run(text=user_input)
    st.write(result)

---

Troubleshooting

• Invalid JSON for --vars: Ensure your input variables are a valid JSON string.
• Template not found: Make sure your template is registered or use --raw.
• API key errors: Set your API keys in .env or as environment variables.
• Test cases file errors: Ensure your test cases file is valid JSON and contains a list of test cases.

---

Further Reading

• docs/cli_usage.md: Full CLI documentation and examples
• docs/genai-workflow-integration.md: How this framework fits into GenAI/LLM workflows
• examples/workflow_integration.py: End-to-end usage demos

---

License

MIT

---

This framework provides a robust, maintainable, and extensible foundation for prompt engineering with LangChain—whether you're experimenting, testing, or deploying LLM-powered applications.