Helicone's guides for building, optimizing, and analyzing LLM applications with Helicone.

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Cookbooks

Practical, hands-on guides for building and optimizing your LLM applications with Helicone.

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LLM Application Development

How to replay LLM sessions

Optimize your AI agents and improve their performance with Helicone’s Session.

How to debug your LLM app

Identify and debug errors in your LLM app with Helicone.

How to build a chatbot with OpenAI structured outputs

This step-by-step tutorial covers function calling, response formatting, and monitoring with Helicone.

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Data Management and Analytics

ETL / data extraction

Extract data from your LLM app with Helicone’s ETL.

Segmenting data with Custom Properties

Derive powerful insights into costs and user behaviors, track environments, user types, and more.

Labeling request data

Make it easier to search and filter your request data in Helicone.

Getting user requests

Retrieve user-specific requests to monitor, debug, and track costs for individual users.

Getting Sessions

Retrieve session data to analyze and replay conversation threads.

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Integration and Environment Management

Environment tracking

Track and manage your development, staging, and production environments.

Integrating Helicone with GitHub Actions

Automate the monitoring and caching of LLM calls in your CI pipelines.

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Knowledge Base

Educational resources to deepen your understanding of LLM concepts and best practices.

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Prompt Engineering

Overview

Learn the basics of prompt engineering and how to craft effective LLM prompts.

Prompt thinking models

Learn how to effectively prompt thinking models like DeepSeek R1 and OpenAI o1/o3.

Be specific and clear

Create concise prompts for better LLM responses.

Use structured formats

Format the generated output for easier parsing and interpretation.

Role-playing

Assign specific roles in system prompts to set the style, tone, and content.

Few-shot learning

Provide examples of desired outputs to guide the LLM towards better responses.

Use constrained outputs

Set clear rules for the model’s responses to improve accuracy and consistency.

Chain-of-thought prompting

Encourage the model to generate intermediate reasoning steps before arriving at a final answer.

Thread-of-thought prompting

Build on previous ideas to maintain a coherent line of reasoning between interactions.

Least-to-most prompting

Break down complex problems into smaller parts, gradually increasing in complexity.

Meta-Prompting

Use LLMs to create and refine prompts dynamically.