# Helicone Guides
> Guides for building, optimizing, and analyzing LLM applications with Helicone.
## How-to Guides
Practical, task-oriented guides for solving specific problems with Helicone. These guides assume you already know the basics and need to accomplish a particular task.
### Data Management & Analytics
Identify and fix errors in your LLM application using Helicone's debugging tools.
Extract and export your LLM data for analysis and reporting.
Track costs and behaviors by environment, user type, and custom dimensions.
Add labels to requests for easier searching and filtering.
Retrieve user-specific requests for monitoring and cost tracking.
Access conversation threads and session history.
### Advanced Features
Replay and analyze past LLM sessions for optimization.
A/B test prompts and model configurations.
Prepare datasets and track fine-tuning workflows.
Set custom request IDs for better tracking.
### Integration & Environment
Separate dev, staging, and production environments.
Monitor LLM calls in your CI/CD pipelines.
Implement custom streaming with the logger SDK.
---
## Tutorials
Step-by-step guides for learning by building complete applications with Helicone. Perfect for understanding how different features work together.
Create a complete AI agent with tool calling, memory, and observability.
Build a multi-model assistant that routes queries based on complexity.
Create an interactive debate app showcasing different integration methods.
Implement comprehensive LLM evaluation using Helicone and Ragas.
Build a chatbot using OpenAI's structured outputs and function calling.
Work with reasoning models like DeepSeek R1 and OpenAI o1/o3.
---
## Knowledge Base
Educational resources to deepen your understanding of LLM concepts and best practices.
### Prompt Engineering
Master the art of crafting effective prompts for optimal LLM performance.
Learn the basics of prompt engineering and how to craft effective LLM
prompts.
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.
---
Not sure which guide to start with? Check out our
Getting Started guide to begin your journey
with Helicone.
Make sure to follow our [official channels](https://www.helicone.ai/updates)
to stay informed about the latest features and updates.