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Understand quality of
LLM applications

Get scores for factual accuracy, context retrieval quality, guideline adherence, tonality, and many more

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>8,00,659 responses evaluated
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Experience the Benefits

See how LLM developers leverage UpTrain to iterate faster and stay ahead of competitors

01
Improve performance by 20%
You can’t improve what you can’t measure. UpTrain continuously monitors your application's performance on multiple evaluation criterions and alerts you in case of any regressions with automatic root cause analysis.
Image showing quality of an LLM application increasing with time along with evaluation scores for hallucinations as well as guideline adherence. Image also shows real-time alerts for cohorts of model under-performance, specifically queries related to pricing information having lower context retrieval quality compared to the mean score.
02
Iterate 3x faster
UpTrain enables fast and robust experimentation across multiple prompts, model providers, and custom configurations, by calculating quantitative scores for direct comparison and optimal prompt selection.
Image showing comparison against two prompt experiments via quantitative scores for hallucinations and tonality. Image also shows cohort-level insights to determine which experiment performs better.
03
Mitigate LLM Hallucinations
Hallucinations have plagued LLMs since their inception. By quantifying degree of hallucination and quality of retrieved context, UpTrain helps to detect responses with low factual accuracy and prevent them before serving to the end-users.
Image showing workflow logic of an LLM application which uses evaluations to determine hallucinations scores and uses that to determine if the LLM response should be shown to the user or redirected to a human.
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Built for developers, by developers

Unleash unparalleled power with a single line of code and tailor every detail as per as your use-case

Icon denoting the diverse LLM evaluations support by UpTrain, covering dimensions such as factual accuracy, groundedness, faithfulness, context relevance, retrieval quality, response relevance, response correctness, guideline adherence, guideline violation, tonality, language quality, response completeness and many more.

Diverse LLM Evaluations

Evaluations to test various aspects of your LLM responses.

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Single-line integration

Single line of code to run LLM evaluations.

Icon denoting customization capabilities of UpTrain where users can configure the evaluation parameters, either define the guideline to follow or define custom model grading prompt for LLM evaluator, or define new custom evaluators to understand the quality and performance of their LLM applications.

Customization

When it comes to AI, there is no one size-fits-all solution.

Icon denoting customization capabilities of UpTrain where users can configure the evaluation parameters, either define the guideline to follow or define custom model grading prompt for LLM evaluator, or define new custom evaluators to understand the quality and performance of their LLM applications.

Cost Efficiency

Single line of code to run LLM evaluations.

When it comes to AI, there is no one size-fits-all solution.

Remarkably Reliable

When it comes to AI, there is no one size-fits-all solution.

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Response_Completeness Score: 1

Measures if the response answers all aspects of the given question

Response_Relevance Score: 0

Measures if the response contains any irrelevant information

Context_Relevance Score: 0

Measures if the queried context has sufficient information to answer the given question

Factual_Accuracy Score: 0

Measures hallucinations i.e. if the response has any made-up information or not with respect to the provided context

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Latest from UpTrain AI

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Pratham Oza | 4th Dec, 2023

Decoding Perplexity and its significance in LLMs

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Pratham Oza | 28th Nov, 2023

Detecting Hallucinations: Notable Techniques for LLMs

Hallucinations in Language Learning Models (LLMs) refer to the phenomenon where the model generates text that is not based on actual data from its training set. Instead, it fabricates information that it believes to be plausible, but is not supported by factual or contextual evidence.

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Dishaa Singhi | 15th Nov, 2023

Revealing the Hidden Truths: The Negative Impacts of Hallucinations in Large Language Models (LLMs)

Large Language Models (LLMs) have undeniably transformed the way we interact with artificial intelligence. They are the cornerstone of applications ranging from healthcare to education, customer support, and sales. The ability of LLMs to understand and generate human-like text has ushered in a new era of innovation. Yet, these models have a problem called hallucinations where they can produce fake or made-up information.

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Pratham Oza | 7th Nov, 2023

Unveiling the Significance of Response Relevance and Completeness in LLMs

We've all marveled at how Large Language Models (LLMs) can tackle questions with uncanny human-like precision. But what if you asked about fixing a leaky faucet and got a history lesson on plumbing instead? It's amusing, but not exactly what you need. You don't want LLMs to answer questions in such a diplomatic way or dodge the actual point like politicians do in press meetings right?

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Shikha Mohanty | 27th Oct, 2023

Lost in Translation: The Critical Impact of Neglecting Guideline Adherence in LLMs

The emergence of Large Language Models (LLMs) has ushered in a new era of possibilities across various industries. From healthcare to customer support and coding assistance, LLMs offer innovative solutions and unprecedented efficiency. With the great power of these AI-driven models comes the responsibility of ensuring they adhere to established guidelines and best practices.

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Shikha Mohanty | 22nd Oct, 2023

Dealing with Hallucinations in LLMs: A Deep Dive

Large Language Models (LLMs) have redefined the way we operate, making information more accessible and humans more efficient. Architectures like RAG have been widely adopted to provide additional context to LLMs for knowledge intensive tasks to improve the factual accuracy of the system. Regardless of these improvements hallucination still remains a key issue to solve for people developing systems based on LLMs.

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Shikha Mohanty | 17th Oct, 2023

Navigating LLM Evaluations: Why It Matters for Your LLM Application

Navigating the world of Large Language Model (LLM) evaluations can be daunting. However, it's crucial to grasp why assessing your LLM application's performance matters. You might wonder, 'Is this step really necessary, or can the model operate without it?'

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Shikha Mohanty | 10th Oct, 2023

LLMs for Enterprises: Why and When to Integrate

LLMs are becoming increasingly popular in the enterprise world, as businesses recognize their potential to revolutionize a wide range of processes. According to a report by Market Research Future, the global LLM market is expected to reach $18.6 billion by 2028.

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Sourabh Agrawal | 1st Oct, 2023

How to Evaluate Your Large Language Model Applications

Large Language Models (LLMs) have emerged as a groundbreaking force and have revolutionized artificial intelligence with their sheer power and sophistication. While being game-changers, they introduce complexities...

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Sourabh Agrawal | 31st Mar, 2023

Fine-tuning Language Models with UpTrain: A Simple Guide to Enhancing Models for Custom Use-cases

The era of large language models (LLMs) taking the world by storm has come and gone. Today, the debate between proponents of bigger models and smaller models has intensified.

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Aryan V S | 20th Mar, 2023

An Introductory Guide to Fine-tuning Large Language Models

A "Language Model" is a machine learning model trained to perform well on tasks related to text/language like Classification, Summarization, Translation, Prediction and Generation.

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Shikha Mohanty | 14th Mar, 2023

Unlocking the Power of Language Models with UpTrain

If you have connected to the internet in the last 60 days, it wouldn’t be of surprise that you have heard of ChatGPT(or atleast come across the word).

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Sourabh & Shri | 7th Mar, 2023

7 Mistakes People Make When Putting Their Models In Production

A critical part of the lifecycle of an ML model is post-production maintenance and performance. Many issues may arise during this period

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