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It is challenging to implement RAG and AI agents effectively in multiple steps. The output of an LLM can be drastically altered by tweaking just a few parameters, such as the definition of a function call or the retrieval parameters. When you write prompts by hand, you have to do a lot of trial and error to get them to work well. Not to mention that updated models frequently render old prompts inoperable.
Meet Hamming AI, a cool start-up that provides a platform for experimentation to help teams in developing trustworthy AI solutions. Hamming AI aims to assist engineering and product teams in developing AI systems that can improve themselves with little to no human intervention.
Major industries, such as the legal, medical, financial, travel, etc., are supported by Hamming AI in their efforts to produce retentive AI products. Collaboration between teams is incorporated into Hamming. When it comes to high-stakes domains, where a wrong answer can result in regulatory ramifications or significant churn, Hamming AI is an expert at helping companies.
To automate prompt engineering, Hamming AI has introduced Prompt Optimizer, a new function now in beta. A variety of prompt variants are generated by Hamming AI using LLMs. Their LLM adjudicator evaluates the effectiveness of a given prompt in completing the assignment. They note extreme cases and apply them to enhance the prompt’s few-shot examples.
Using Hamming AI products, you can create industry-specific retentive AI products, such as:
Organize golden datasets with versioning already implemented.
Transform traces into test cases easily and incorporate them into your golden dataset.
RAG optimized scores to locate pipeline bottlenecks rapidly.
Hamming AI system will evaluate the pipeline’s performance on every dataset using our proprietary scores for accuracy, tone, hallucinations, precision, and recall.
The purpose of Hamming is to provide an environment conducive to testing AI products. It automates the evaluation process by leveraging large language models (LLMs). What Hamming does is practically the same as a group of AI specialists carefully reviewing the results of an AI model. Compared to the hours it would take to test various configurations and datasets manually, this automated evaluation is a huge time saver for developers. When compared to the old-fashioned method of human review, Hamming claims to be 20 times faster and ten times cheaper.
To sum it up
There are several advantages to using Hamming beyond its speed and efficiency. Teams can be sure they’re using the most recent data thanks to dataset versioning, and they can see exactly how different iterations compare thanks to experiment tracking. On top of that, Hamming allows developers to create metrics that are perfect for their requirements.
The post Hamming AI: An AI Startup that Provides Fastest Way to Make Your Prompts, RAG, and AI Agents More Reliable appeared first on MarkTechPost.
“}]] [[{“value”:”It is challenging to implement RAG and AI agents effectively in multiple steps. The output of an LLM can be drastically altered by tweaking just a few parameters, such as the definition of a function call or the retrieval parameters. When you write prompts by hand, you have to do a lot of trial and
The post Hamming AI: An AI Startup that Provides Fastest Way to Make Your Prompts, RAG, and AI Agents More Reliable appeared first on MarkTechPost.”}]] Read More AI Shorts, AI Startups, Applications, Artificial Intelligence, Editors Pick, Large Language Model, Staff, Tech News, Technology