Seeking Accuracy with LLMs

Large Language Models (LLMs) like GPT-4, Gemini, and open-source models like Mistral, have revolutionized human-computer interaction, impacting everything from customer support to creative writing. However, while their generative capabilities are impressive, relying solely on LLMs for accuracy-critical tasks can be risky. This is where FIY steps in, offering a specialized approach to harness the power of LLMs while ensuring accuracy in troubleshooting and maintenance.

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The Allure and Limitations of LLMs:

LLMs are trained on massive datasets, learning to predict the most probable next word in a sequence based on the given context. This enables them to generate human-quality text, translate languages, and answer questions convincingly. However, their accuracy is limited by:

• Training Data: LLMs are only as good as the data they are trained on. Datasets like Common Crawl, while vast, often truncate PDFs, leading to incomplete or inaccurate information.
• Hallucinations: LLMs can generate plausible-sounding but factually incorrect information, a phenomenon known as "hallucination." This poses significant risks in applications like troubleshooting where accuracy is paramount.

To illustrate this with a simple example, observe the following conversation. Obviously, the prompt biases towards finding the letter ‘m’ in the word ‘weather’, to which the model instantly complies. This exposes issues where models are both a) easily biased upon by the quality of the prompt and b) require follow-up to identify the mistake.

Similarly, imagine asking an LLM for troubleshooting steps from a large PDF manual. If the manual was truncated in the training data, the LLM might confidently provide inaccurate or incomplete information, potentially leading to further damage or safety hazards.

The correct, complete response as found in the manual is actually much simpler, but the generated response does not many any mention of either the contactor box or the fuse bracket. Here's a screenshot from the manual - simple and more accurate:

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FIY: A Specialized Approach for Accuracy:

FIY addresses these limitations by focusing on semantic search and LLM-agnostic infrastructure:

• Specialization in Troubleshooting: FIY is designed specifically for troubleshooting and maintenance, framing conversations to efficiently identify problems and solutions.
• High-Quality Semantic Search: FIY utilizes multimodal search across complete PDFs, ensuring access to accurate and comprehensive information. This retrieved information is then passed to LLMs for post-processing.
• LLM Agnostic Infrastructure: FIY's API is not tied to a specific LLM. This allows FIY to leverage the latest and most accurate models, ensuring high-quality responses.

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Benefits of FIY:

• Reduced Hallucinations: By accessing complete and accurate information, FIY minimizes the risk of LLM hallucinations, leading to safer and more reliable troubleshooting.
• Enhanced Accuracy: FIY's semantic search capabilities ensure that users receive the most relevant and accurate information from trusted sources.
• Improved Efficiency: FIY streamlines the troubleshooting process, enabling faster problem resolution and reduced downtime.

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While LLMs offer incredible potential, accuracy remains a critical concern in specific applications. FIY leverages the power of LLMs while addressing their limitations, providing a specialized solution for accurate and efficient troubleshooting and maintenance. By combining high-quality semantic search with LLM-agnostic infrastructure, FIY empowers users with the information they need to confidently solve problems and maintain critical systems.