GPT 4.5 Released: Here Are the Benchmarks
This article covers everything you need to know about GPT 4.5. We go over the technical details, benchmarks and real-world reviews and some developer guidelines on when to use it.
Technical Review: Claude 3.7 Sonnet & Claude Code for Developers
In this blog, we take a deep dive into Claude 3.7 Sonnet's reasoning capabilities and the new Claude Code CLI tool. Does its coding performance stack up against other popular models? Let's find out.
Grok 3 Technical Review: Everything You Need to Know
Grok 3 claims to be the 'Smartest AI in the world' with 10-15x more compute and advanced reasoning. We analyze its benchmarks, real-world performance, and how it stacks up against GPT-4, Claude, and Gemini.
OpenAI Deep Research: How it Compares to Perplexity and Gemini
A deep dive into OpenAI's latest research model, how it stacks up against Perplexity and Gemini and a list of free open-source alternatives.
Introducing Helicone V2: A Complete Development Lifecycle for LLM Applications
Here's how Helicone V2 helps teams build better LLM applications through comprehensive logging, evaluation, experimentation, and release workflows.
Janus Pro Released: How to Access DeepSeek's Unified Multimodal Model
DeepSeek Janus Pro is a multimodal AI model designed for both text and image processing. In this guide, we will walk through the model's capabilities, benchmarks, and how to access it.
How to safely switch your production apps to DeepSeek
In this guide, we cover how to perform regression testing, compare models, and transition to DeepSeek with real production data without impacting users.
How to Prompt Thinking Models like DeepSeek R1 and OpenAI o3
Prompting thinking models like DeepSeek R1 and OpenAI o3 requires a different approach than traditional LLMs. Learn the key do's and don'ts for optimizing your prompts, and when to use structured outputs for better results.
Top Open WebUI Alternatives for Running LLMs Locally
Looking for Open WebUI alternatives? We will cover self-hosted platforms like HuggingChat, AnythingLLM, LibreChat, Ollama UI, and more, and show you how to set up your environment in minutes.
Top 10 AI Inference Platforms in 2025
Discover the top AI inferencing platforms of 2025, including Together AI, Fireworks AI, Hugging Face, and more. Compare features, pricing, and benefits of top OpenAI alternatives.
How to Implement Effective LLM Caching
A deep dive into effective caching strategies for building scalable and cost-efficient LLM applications, covering exact key vs. semantic caching, architectural patterns, and practical implementation tips.
A Developer's Guide to Preventing Prompt Injection
A comprehensive guide on preventing prompt injection in large language models (LLMs), where we cover practical strategies to protect and safeguard your AI applications.
DeepSeek-V3 Release: New Open-Source MoE Model
A deepdive into DeepSeek-V3, the 671B parameter open-source MoE model that rivals GPT-4 at fraction of the cost. Compare benchmarks, deployment options, and real-world performance metrics.
Top Prompt Evaluation Frameworks in 2025: Helicone, OpenAI Eval, and More
In this blog, we will compare leading prompt evaluation frameworks, including Helicone, OpenAI Eval, PromptFoo, and more. Learn about which evaluation framework best suits your needs and the basics setups.
OpenAI o3 Released: Benchmarks and Comparison to o1
OpenAI just launched the o3 and o3-mini reasoning models. These models are built on the foundation of OpenAI's o1 models, introducing several notable improvements in performance, reasoning capabilities, and testing results.
GPT-4o Mini vs. Claude 3.5 Sonnet: A Detailed Comparison for Developers
GPT-4o mini performs surprisingly well on many benchmarks despite being a smaller model, often standing nearly on par with Claude 3.5 Sonnet. Let's compare them.
Tree-of-Thought Prompting: Key Techniques and Use Cases
Learn about Tree-of-Thought (ToT) prompting techniques, how it works and how it compares with other prompting techniques like Chain-of-Thought (CoT).
Building a Simple Chatbot with OpenAI Structured Outputs
Learn how to use OpenAI's new Structured Outputs feature to build a reliable flight search chatbot. This step-by-step tutorial covers function calling, response formatting, and monitoring with Helicone.
Helicone vs Traceloop: Best Tools for Monitoring LLMs
In this guide, we compare Helicone and Traceloop's key features, pricing, and integrations to find the best LLM monitoring platform for your production needs.
Helicone vs Comet: Best Open-Source LLM Evaluation Platform
A detailed comparison of Helicone and Comet Opik for LLM evaluation. Here are the key features, differences and how to choose the right platform for your team's needs.
Comparing Helicone vs. Honeyhive for LLM Observability
We compare Helicone and HoneyHive, two leading observability and monitoring platforms for large language models, and find which one is right for you.
Text Classification with LLMs: Approaches and Evaluation Techniques For Developers
Explore the top methods for text classification with Large Language Models (LLMs), including supervised vs unsupervised learning, fine-tuning strategies, model evaluation, and practical best practices for accurate results.
Chain-of-Thought Prompting: Techniques, Tips, and Code Examples
Learn about Chain-of-Thought (CoT) prompting, its techniques (zero-shot, few-shot, and auto-CoT), tips and real-world applications. See how it compares to other methods and discover how to implement CoT prompting to improve your AI application's performance.
Top 10 AI Inference Platforms in 2025
Discover the top AI inferencing platforms of 2025, including Together AI, Fireworks AI, Hugging Face, and more. Compare features, pricing, and benefits of top OpenAI alternatives.
Chunking Strategies For Production-Grade RAG Applications
Optimize your RAG-powered application with semantic and agentic chunking. Learn about their limitation, and when to use them.
Gemini 2.0 Flash Explained: Building More Reliable Applications
Google has released Gemini 2.0 Flash Thinking, a direct competitor to OpenAI's o1 and a breakthrough in AI models with transparent reasoning. Compare features, benchmarks, and limitations.
Comparing CrewAI vs. Dify - Which is the Best AI Agent Framework?
What's the difference between CrewAI and Dify? Here's a comprehensive comparison of their main features, use cases and how developers can monitor their agents with Helicone.
Claude 3.5 Sonnet vs OpenAI o1: A Comprehensive Comparison
Discover how Claude 3.5 Sonnet compares to OpenAI o1 in coding, reasoning, and advanced tasks. See which model offers better speed, accuracy, and value for developers.
Google's Gemini-Exp-1206 is Outperforming GPT-4o and o1
Released in December 2024, Gemini-Exp-1206 is quickly beating the performance of OpenAI gpt-4o, o1, claude 3.5 Sonnet and Gemini 1.5. Delve into key features, benchmarks, applications and what the hype is all about.
Llama 3.3 just dropped — is it better than GPT-4 or Claude-Sonnet-3.5?
Meta just released their newest AI model with significant optimizations in performance, cost efficiency, and multilingual support. Is it truly better than its predecessors and the top models in the market?
O1 and ChatGPT Pro — here's everything you need to know
OpenAI has recently made two significant announcements: the full release of their o1 reasoning model and the introduction of ChatGPT Pro, a new premium subscription tier. Here's a TL;DR on what you missed.
GPT-5: Release Date, Features & Everything You Need to Know
GPT-5 is the next anticipated breakthrough in OpenAI's language model series. Although its release is slated for early 2025, this guide covers everything we know so far, from projected capabilities to potential applications.
How to test your LLM prompts (with examples)
How do you measure the quality of your LLM prompts and outputs? In this blog, we talk about how you can evaluate LLM performance and effectively test your prompts.
Prompt Evaluation Explained: Random Sampling vs. Golden Datasets
Crafting high-quality prompts and evaluating them requires both high-quality input variables and clearly defined tasks. In a recent webinar, Nishant Shukla, the senior director of AI at QA Wolf, and Justin Torre, the CEO of Helicone, shared their insights on how they tackled this challenge.
Comparing CrewAI vs. AutoGen for Building AI Agents
CrewAI and AutoGen are two notable frameworks in the AI agent landscape. We will cover the key differences, example implementations and share our recommendations if you are starting out in agent-building.
Building a RAG-Powered PDF Chatbot with LLMs and Vector Search
Build a smart chatbot that can understand and answer questions about PDF documents using Retrieval-Augmented Generation (RAG), LLMs, and vector search. Perfect for developers looking to create AI-powered document assistants.
Choosing Between LlamaIndex and LangChain
Building AI agents but not sure which of LangChain and LlamaIndex is a better option? You're not alone. We find that it’s not always about choosing one over the other.
The Case Against Fine-Tuning
Discover the strategic factors for when and why to fine-tune base language models like LLaMA for specialized tasks. Understand the limited use cases where fine-tuning provides significant benefits.
Debugging RAG Chatbots and AI Agents with Sessions
Debugging AI agents can be difficult, but it doesn't have to be. In this guide, we explore common AI agent pitfalls, how to debug multi-step processes using Helicone's Sessions, and the best tools for building reliable, production-ready AI agents.
Braintrust Alternative? Braintrust vs Helicone
Compare Helicone and Braintrust for LLM observability and evaluation in 2024. Explore features like analytics, prompt management, scalability, and integration options. Discover which tool best suits your needs for monitoring, analyzing, and optimizing AI model performance.
Optimizing AI Agents: How Replaying LLM Sessions Enhances Performance
Learn how to optimize your AI agents by replaying LLM sessions using Helicone. Enhance performance, uncover hidden issues, and accelerate AI agent development with this comprehensive guide.
What We've Shipped in the Past 6 Months
Join us as we reflect on the past 6 months at Helicone, showcasing new features like Sessions, Prompt Management, Datasets, and more. Learn what's coming next and a heartfelt thank you for being part of our journey.
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Prompt Engineering Tools & Techniques [Updated Jan 2025]
Writing effective prompts is a crucial skill for developers working with large language models (LLMs). Here are the essentials of prompt engineering and the best tools to optimize your prompts.
Five questions to determine if LangChain fits your project
Explore five crucial questions to determine if LangChain is the right choice for your LLM project. Learn from QA Wolf's experience in choosing between LangChain and a custom framework for complex LLM integrations.
7 Awesome Platforms & Frameworks for Building AI Agents (Open-Source & More)
Explore the top platforms for creating AI agents, including Dify, AutoGen, and LangChain. Compare features, pros and cons to find the ideal framework.
Portkey Alternatives? Portkey vs Helicone
Compare Helicone and Portkey for LLM observability in 2024. Explore features like analytics, prompt management, caching, and integration options. Discover which tool best suits your needs for monitoring, analyzing, and optimizing AI model performance.
5 Powerful Techniques to Slash Your LLM Costs by Up to 90%
Building AI apps doesn't have to break the bank. We have 5 tips to cut your LLM costs by up to 90% while maintaining top-notch performance—because we also hate hidden expenses.
Behind 900 pushups, lessons learned from being #1 Product of the Day
By focusing on creative ways to activate our audience, our team managed to get #1 Product of the Day.
How to Win #1 Product of the Day on Product Hunt
Discover how to win #1 Product of the Day on Product Hunt using automation secrets. Learn proven strategies for automating user emails, social media content, and DM campaigns, based on Helicone's successful launch experience. Boost your chances of Product Hunt success with these insider tips.
Helicone vs. Arize Phoenix: Which is the Best LLM Observability Platform?
Compare Helicone and Arize Phoenix for LLM observability in 2024. Explore open-source options, self-hosting, cost analysis, and LangChain integration. Discover which tool best suits your needs for monitoring, debugging, and improving AI model performance.
Langfuse Alternatives? Langfuse vs Helicone
Compare Helicone and Langfuse for LLM observability in 2024. Explore features like analytics, prompt management, caching, and self-hosting options. Discover which tool best suits your needs for monitoring, analyzing, and optimizing AI model performance.
4 Essential Helicone Features to Optimize Your AI App's Performance
This guide provides step-by-step instructions for integrating and making the most of Helicone's features - available on all Helicone plans.
How to redeem promo codes in Helicone
On August 22, Helicone will launch on Product Hunt for the first time! To show our appreciation, we have decided to give away $500 credit to all new Growth user.
The Emerging LLM Stack: A New Paradigm in Tech Architecture
Explore the emerging LLM Stack, designed for building and scaling LLM applications. Learn about its components, including observability, gateways, and experiments, and how it adapts from hobbyist projects to enterprise-scale solutions.
The Evolution of LLM Architecture: From Simple Chatbot to Complex System
Explore the stages of LLM application development, from a basic chatbot to a sophisticated system with vector databases, gateways, tools, and agents. Learn how LLM architecture evolves to meet scaling challenges and user demands.
The Ultimate Guide to Effective Prompt Management
Effective prompt management is the #1 way to optimize user interactions with large language models (LLMs). We explore the best practices and tools for effective prompt management.
Meta Releases SAM 2 and What It Means for Developers Building Multi-Modal AI
Meta's release of SAM 2 (Segment Anything Model for videos and images) represents a significant leap in AI capabilities, revolutionizing how developers and tools like Helicone approach multi-modal observability in AI systems.
What is LLM Observability and Monitoring?
Learn about how LLM observability differs from traditional observability, key challenges in building with LLM and best practices for monitoring LLM applications.
Compare: The Best LangSmith Alternatives & Competitors
Observability tools allow developers to monitor, analyze, and optimize AI model performance, which helps overcome the 'black box' nature of LLMs. But which LangSmith alternative is the best in 2024? We will shed some light.
Handling Billions of LLM Logs with Upstash Kafka and Cloudflare Workers
We desperately needed a solution to these outages/data loss. Our reliability and scalability are core to our product.
Best Practices for AI Developers: Full Guide (June 2024)
Achieving high performance requires robust observability practices. In this blog, we will explore the key challenges of building with AI and the best practices to help you advance your AI development.
I built my first AI app and integrated it with Helicone
So, I decided to make my first AI app with Helicone - in the spirit of getting a first-hand exposure to our user's pain points.
How to Understand Your Users Better and Deliver a Top-Tier Experience with Custom Properties
In today's digital landscape, every interaction, click, and engagement offers valuable insights into your users' preferences. But how do you harness this data to effectively grow your business? We may have the answer.
Helicone vs. Weights and Biases
Training modern LLMs is generally less complex than traditional ML models. Here's how to have all the essential tools specifically designed for language model observability without the clutter.
Insider Scoop: Our Co-founder's Take on GitHub Copilot
No BS, no affiliations, just genuine opinions from Helicone's co-founder.
Insider Scoop: Our Founding Engineer's Take on PostHog
No BS, no affiliations, just genuine opinions from the founding engineer at Helicone.
A step by step guide to switch to gpt-4o safely with Helicone
Learn how to use Helicone's experiments features to regression test, compare and switch models.
An Open-Source Datadog Alternative for LLM Observability
Datadog has long been a favourite among developers for its application monitoring and observability capabilities. But recently, LLM developers have been exploring open-source observability options. Why? We have some answers.
A LangSmith Alternative that Takes LLM Observability to the Next Level
Both Helicone and LangSmith are capable, powerful DevOps platform used by enterprises and developers building LLM applications. But which is better?
Why Observability is the Key to Ethical and Safe Artificial Intelligence
As AI continues to shape our world, the need for ethical practices and robust observability has never been greater. Learn how Helicone is rising to the challenge.
Introducing Vault: The Future of Secure and Simplified Provider API Key Management
Helicone's Vault revolutionizes the way businesses handle, distribute, and monitor their provider API keys, with a focus on simplicity, security, and flexibility.
Life after Y Combinator: Three Key Lessons for Startups
From maintaining crucial relationships to keeping a razor-sharp focus, here's how to sustain your momentum after the YC batch ends.
Helicone: The Next Evolution in OpenAI Monitoring and Optimization
Learn how Helicone provides unmatched insights into your OpenAI usage, allowing you to monitor, optimize, and take control like never before.
Helicone partners with AutoGPT
Helicone is excited to announce a partnership with AutoGPT, the leader in agent development.
Generative AI with Helicone
In the rapidly evolving world of generative AI, companies face the exciting challenge of building innovative solutions while effectively managing costs, result quality, and latency. Enter Helicone, an open-source observability platform specifically designed for these cutting-edge endeavors.
(a16z) Emerging Architectures for LLM Applications
Large language models are a powerful new primitive for building software. But since they are so new—and behave so differently from normal computing resources—it's not always obvious how to use them.
(Sequoia) The New Language Model Stack
How companies are bringing AI applications to life