How Conversation Memory Improves Conversation experience

Summary of Observations and Analysis

The experiment compared two configurations for generating search queries in a conversational AI system: one with memory (including 3 past messages from the conversation) and one without memory. The following key observations were made:

Key Characteristics:

1. Domain Anchoring:

   • With Memory: Queries remain anchored to the specific domain (e.g., EU AI Act, CV screening tool).
   • Without Memory: Queries lose context after the initial message and become more generic.

2. Pronoun & Reference Resolution:

   • With Memory: Pronouns and references are resolved to concrete entities.
   • Without Memory: References remain ambiguous and require restating context.

3. Terminology Preservation:

   • With Memory: Specific technical terms persist across queries.
   • Without Memory: Specific terms tend to drop out, replaced by vague synonyms.

4. Query Specificity:

   • With Memory: Queries become more focused and refined over turns.
   • Without Memory: Queries become generic over subsequent turns.

5. Retrieval Precision:

   • With Memory: Higher precision with more relevant results.
   • Without Memory: Lower precision with more irrelevant results.

6. **

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