Langchain evaluation: What is it, who is it for, and why run it?

As large language models and AI-integrated products become increasingly popular and complex, evaluating their performance and accuracy presents a new challenge in the space.

The evaluation of AI products has yet to catch up with the speed at which AI product development is advancing. To get a grasp of this, let’s understand the current approach:

Does your evaluation process resemble the repetitive loop of running LLM apps on a list of prompts, manually inspecting outputs, and attempting to gauge quality based on each input?

If so, we need to recognize that evaluation is not a one-time tick-off of to-do list item but a multi-step constant iteration that determines the performance and longevity of your LLM app.

In this article, we’ll discuss how we actualize this iterative process with tools like Langchain Evaluation and Phospho, which allow for optimizing performance through AI product analytics.

Understanding Langchain

Langchain is an open-source framework that was created in 2022 and has made it much easier for independent builders to create AI applications.

The reason it's given a pioneering sentiment is that traditionally, before long-chain, building AI products called for the expertise and a lot of resources. Imagine it like LEGO blocks for AI, you can piece together different components to create more complex and capable applications so you don’t have to start from scratch. Langchain democratized the process by allowing developers to focus on creativity rather than getting bogged down in technical complexity with its easy integrations and open-source ecosystem.

The framework became really popular, both startups and tech giants started leveraging it to quickly prototype and deploy AI features and products. The flexibility allowed for multiple use cases ranging from (but not limited to) chatbots, virtual assistants, content generation tools, and more robust data analytics platforms.

OpenAI has partnered with Langchain for some projects and competing startups like Anthropic have actually integrated Langchain into their workflows. Google and Microsoft are also transparent about their increasing interest in exploring its potential.

Developing AI products with Langchain is one thing, but fine tuning with AI analytics is another. With more and more projects developing AI and integrating its capabilities into features, we have come across a new challenge: how do we measure and improve the performance of these products?

What is Langchain Evaluation?

Langchain evaluation is a process that exists to accomplish the above challenge by assessing performance, reliability and efficiency across key metrics such as response accuracy, processing speed, memory usage and error rates.

Given the newness and inherent uncertainties surrounding LLM features, a fast and well informed iteration cycle for the release of these product features is imperative to uphold privacy and responsibility standards as well as ultimately providing a better user experience with optimised performance.

But there’s a blocker for a lot of AI apps as Langchain evaluation is specifically designed for products built on top of the Langchain network, which leaves a large portion of the AI market without access to important and much needed product insights.

Challenges in Langchain Evaluation

Not everyone uses Langchain, many AI products are built using other frameworks and therefore require a more flexible approach to AI product analytics. There are some limitations with current alternatives:

Technical complexity: which shuts out non-technical team members.

Lack of personalized KPIs: one size fits all metrics don’t cut it for specialized AI apps.

Difficulty with integration: ease of integration with popular development environments is not a forefront concern with current alternatives.

The demand for more flexible and accessible comprehensive analytics across various AI products isn’t just coming from tech giants, startups and mid size companies are also realising the discrepancy in ease of production and difficulty in evaluation, resulting in the need for better AI analytics to improve their product and compete in the market.

This is why we built phospho, to address the noticeable gap in AI product analytics between what’s needed and what’s actually available.

Introducing Phospho

Phospho is an open-source text analytics platform designed to extract real-time insights for more streamlined iteration cycles aligned with user needs for LLM integrated apps.

Our platform enables teams to flexibly monitor user interactions in real-time at scale, detect issues, and recognize patterns to optimize your AI products regardless of your chosen tech stack or technical expertise within product teams.

To cater for varying needs and development environments across different startups, we also intentionally made it as versatile and user friendly as possible to ensure accessibility to both tech and non tech people (such as some Product Managers) which is also reflected with our key features:

• Real-time monitoring of user interactions lets you track and log user inputs to identify issues or trends and continuously fine-tune the performance of your LLM app.
• Automated insights extraction and KPI detection so you can create your own KPIs and custom criteria to ‘flag’ for, and you can label if it was a successful or unsuccessful interaction.
• A/B tests different versions of your LLM app to see which ones perform better with your users.
• Continuous evaluation and iteration support. You can use our automatic evaluation pipeline that runs continuously to keep improving your AI model’s performance.
• Easily integrates with the most popular tech stacks, simply add Phospho with any popular tools and languages like JavaScript, Python, CSV, OpenAI, LangChain, and Mistral.

By using Phospho’s features effectively, we aim to help modern AI startups handle the complexities of obtaining usage data to inform faster development cycles without the need for big budgets or stitching together multiple tools and apps with painful learning curves.

This keeps product teams connected to the user experience and helps product managers in particular understand what needs to be optimised or fixed to better solve customer problems, increase AI product performance, and remain competitive in the fast-changing landscape of the LLM app market.

Why use phospho

The textual data contained inside LLM apps are a goldmine for insights and it’s critically important to get the right data to iterate faster and ultimately take a bigger proportion of the market.

However, most product analytics tools, which have seen a huge boom over the last decade, were not designed to derive insights from textual data from LLM apps.

This is where we at Phospho have provided the tools to extract rich insights from your users’ text analytics to iterate effectively and quickly with data driven decisions.

Our advanced text analytics capabilities offer a competitive edge in providing more enriched user understanding for faster and more effective development cycles aligned with their needs and pains.

If you want to understand your users closely and optimize your LLM app without siloing your nontechnical team members, sign up here and try out Phospho on your own data. It’s as simple as importing a CSV or Excel file!

The Future of AI Product Analytics: Langchain VS Phospho

The AI product landscape is evolving faster than we think, and the tools we use to measure and improve our products need to keep pace. While Langchain offers great ease in developing AI products quickly, it falls short in accessible and comprehensive evaluation tools that closer meets the needs of agile startups building LLM apps.

The demand for tools like Phospho that can work across different frameworks and tech stacks provides this for every startup, not just those built on Langchain. Without robust, more advanced and accessible AI product analytics, we risk missing critical insights, trends, and opportunities for improvement in our LLM applications.

it’s important to note that the fast-paced evolution of technology in Artificial Intelligence may introduce new metrics and frameworks to push the needs and development of AI product analytics further. This would only reinforce the need for comprehensive and flexible tools like Phospho even more in order to properly address the demands of various startups and evolving markets.

We encourage you to stay informed about the latest updates in the field of LLM app evaluation and how our latest product developments reflect that by subscribing to this blog below or simply trying Phospho with your own data by signing up here.