Question and answering applications are the most useful applications of generative AI
Let's look at this most promising use-case of generative AI👇
What is a Q&A application using LLMs?
A question and answering (Q&A) application using large language models involves utilizing advanced natural language processing techniques, particularly those offered by large language models like GPT-3.5.
learn more: bit.ly/47H9DL6
These applications aim to automatically generate accurate and relevant answers to user-provided questions in a human-like manner.
This approach is flexible and far better than fine-tuning custom LLM because this is much more cost-effective than letter one.
Q&A application workflow:
1. Document loaders: To load user documents for vectorization and storage purposes
2. Text splitters: These are the document transformers that transform documents into fixed chunk lengths to store them efficiently
3. Vector storage: Vector database integrations to store vector embeddings of the input texts
4. Document retrieval: To retrieve texts based on user queries to the database. They use similarity search techniques to retrieve the same.
5. Model output: Final model output to the user query generated from the input prompt of query and retrieved texts.
Depiction of an entire workflow👇
Advantages of Custom Q&A Applications Over a Model Fine-tuning
1. Context-specific answers
2. Adaptable to new input documents
3. No need to fine-tune the model, which saves the cost of model training
4. More accurate and specific answers rather than general answers
If you found this interesting then, check out the full article I wrote on Analytics Vidhya - India's leading Data Science and AI platform👇
analyticsvidhya.com/blog/2023/08/qa-applications/
Avi Kumar Talaviya
@avikumart_
Simplifying Data Science and Machine learning for beginners🤖 I share valuable threads & resources on DS/ML/DL @kaggle Master|Python|ML|Data|Analytics|Tech