• test reading to confirm the Senate.ca method is still effective
• ...

add the Prompt

Yes, the method you described resembles a Retrieval-Augmented Generation (RAG) approach, which combines retrieval of information from a database or knowledge source with generative capabilities to produce relevant responses.

Here's how you might implement this method:

1. **Initial Dictionary Lookup**: Start by querying a dictionary for the word you are interested in. This will provide you with the various meanings, usages, and contexts of the word.

2. **Extract Meanings**: From the initial lookup, extract the different possible meanings of the word. This could involve identifying definitions, synonyms, antonyms, and example sentences.

3. **Subsequent Lookups**:
   - For each meaning extracted, perform additional lookups. This could involve checking synonyms or related words to gain deeper understanding or context.
   - You might also look up antonyms or words that commonly appear in similar contexts.

4. **Contextual Generation**: Use the information gathered from these lookups to generate a response or provide a comprehensive explanation of the word, including its nuances and applications in different contexts.

### Example of Implementation Steps:

- **Word Entry**: Start with the word "bank."
- **Initial Lookup**: Identify meanings such as "financial institution," "side of a river," etc.
- **Extract Meanings**: Create a list of meanings.
- **Subsequent Lookups**:
  - For "financial institution," look up related terms like "savings," "loan," "interest."
  - For "side of a river," explore terms like "banking," "shore," "floodplain."
- **Generate Output**: Combine the information to explain the word's varied meanings and contexts.

This approach enhances the depth and relevance of the information provided, making it a practical application of RAG techniques in natural language processing.