Guides for building, optimizing, and analyzing LLM applications with Helicone.
Practical, hands-on guides for building and optimizing your LLM applications with Helicone.
Optimize your AI agents and improve their performance with Helicone’s Session.
Identify and debug errors in your LLM app with Helicone.
This step-by-step tutorial covers function calling, response formatting, and monitoring with Helicone.
Extract data from your LLM app with Helicone’s ETL.
Derive powerful insights into costs and user behaviors, track environments, user types, and more.
Make it easier to search and filter your request data in Helicone.
Retrieve user-specific requests to monitor, debug, and track costs for individual users.
Retrieve session data to analyze and replay conversation threads.
Track and manage your development, staging, and production environments.
Automate the monitoring and caching of LLM calls in your CI pipelines.
Educational resources to deepen your understanding of LLM concepts and best practices.
Learn how to effectively prompt thinking models like DeepSeek R1 and OpenAI o1/o3.
Create concise prompts for better LLM responses.
Format the generated output for easier parsing and interpretation.
Assign specific roles in system prompts to set the style, tone, and content.
Provide examples of desired outputs to guide the LLM towards better responses.
Set clear rules for the model’s responses to improve accuracy and consistency.
Encourage the model to generate intermediate reasoning steps before arriving at a final answer.
Build on previous ideas to maintain a coherent line of reasoning between interactions.
Break down complex problems into smaller parts, gradually increasing in complexity.
Use LLMs to create and refine prompts dynamically.
Need help choosing a guide?
Not sure which guide to start with? Check out our Getting Started guide to begin your journey with Helicone.
Guides for building, optimizing, and analyzing LLM applications with Helicone.
Practical, hands-on guides for building and optimizing your LLM applications with Helicone.
Optimize your AI agents and improve their performance with Helicone’s Session.
Identify and debug errors in your LLM app with Helicone.
This step-by-step tutorial covers function calling, response formatting, and monitoring with Helicone.
Extract data from your LLM app with Helicone’s ETL.
Derive powerful insights into costs and user behaviors, track environments, user types, and more.
Make it easier to search and filter your request data in Helicone.
Retrieve user-specific requests to monitor, debug, and track costs for individual users.
Retrieve session data to analyze and replay conversation threads.
Track and manage your development, staging, and production environments.
Automate the monitoring and caching of LLM calls in your CI pipelines.
Educational resources to deepen your understanding of LLM concepts and best practices.
Learn how to effectively prompt thinking models like DeepSeek R1 and OpenAI o1/o3.
Create concise prompts for better LLM responses.
Format the generated output for easier parsing and interpretation.
Assign specific roles in system prompts to set the style, tone, and content.
Provide examples of desired outputs to guide the LLM towards better responses.
Set clear rules for the model’s responses to improve accuracy and consistency.
Encourage the model to generate intermediate reasoning steps before arriving at a final answer.
Build on previous ideas to maintain a coherent line of reasoning between interactions.
Break down complex problems into smaller parts, gradually increasing in complexity.
Use LLMs to create and refine prompts dynamically.
Need help choosing a guide?
Not sure which guide to start with? Check out our Getting Started guide to begin your journey with Helicone.