LLMs give you the flexibility of natural languages to express your query and format your answers.
But this comes with the same flaws as human interactions: fuzzy interpretations and lack of truthfulness.
By plugging a database before/after the query/answers, one can build more than necessary safeguards.
An example is Question & Answering with a vector database:
– Use a SBERT model to vectorize the query
– Retrieve some context from the query in the database with cosine similarity
– Build a prompt from the query and context
– Send the prompt to the QnA model
In that example, the context is retrieved from a previously indexed database.
For instance from WooCommerce products descriptions and attributes.
This limits the answers to a subset of the model’s universe, the one indexed in the database.
A step further is to fine-tune the model.
And another step further is to fully train the model, including the tokenizer: this will restrict even more answers with a limited vocabulary (for safety or specialised domains like mathematics or chemistry or finance or legal).
Good news!
You can already search or QnA your WooCommerce shop with the Weaviate database, and OpenAI | Cohere | Hugging Face LLM models: https://wpsolr.com
