LLM for Retrieval Augmented Generation (RAG) Applications

Now, finally after completing all the necessary steps, we have reached the most important part of our journey: where we start utilizing the remarkable capabilities of Large Language Models (LLMs) to generate responses to user queries.

Throughout our ASKTOPDF development journey, we have traversed various stages, from preprocessing data to implementing advanced techniques like data chunking and leveraging vector embeddings and databases.

Harnessing the Potential of LLMs

Large Language Models represent a cutting-edge advancement in natural language processing, capable of understanding and generating human-like text with accuracy and fluency. By integrating LLMs into ASKTOPDF, we empower our application to comprehend user queries in natural language and provide intelligent responses, elevating the user experience.

The Role of OpenAI Models

In the context of ASKTOPDF, we’ve chosen to utilize OpenAI Models for both embeddings and responding to user queries. These state-of-the-art language models have been trained on vast amounts of text data, enabling them to understand the nuances of human language and provide contextually relevant responses. By leveraging OpenAI Models, we ensure that ASKTOPDF delivers accurate and insightful answers to user questions, enhancing the overall utility and effectiveness of our application.

For embeddings we have use text-embedding-3-small
For Generating response we have use gpt-3.5-turbo

How to get the most out of GPT-3.5 model

To make the most of the GPT-3.5/LLM model, how you ask it for information is really important. This is called prompting. Prompting is like giving the model instructions on how to understand your question and give you a good answer.
Here are some tips for asking the model questions in a way that gets you the best results:

Guideline 1: Clear and Specific Instructions
To ensure precise model responses, employ clear instructions with delimited sections for distinct inputs.

Guideline 2: Allow Time for Thought
Specifying step-by-step instructions, allowing the model to reason through tasks. Instruct the model to compute its solution before evaluating the provided answers for enhanced accuracy.

Guideline 3: Structured Output Request
Request structured outputs like JSON to facilitate data processing and interpretation. Ensure uniform formatting to streamline model outputs.

Guideline 4: Avoiding Hallucinations
Reduce the risk of fabricated responses by anchoring prompts to factual context. Source quotes from relevant texts to guide model responses and minimize misleading outputs.

Example Prompt

"""You must answer in the "{language}" language.
    Combine the following text into a cohesive document:
    "{text}"
    Write using clear markdown formatting in a style expected of the goal "{goal}".
    Be as clear, informative, and descriptive as necessary.
    You will not make up information or add any information outside of the above text.
    Only use the given information and nothing more.

    If there is no information provided, say "There is nothing to summarize".
    """,

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