The Invisible Journey of Your AI Query
When interacting with generative AI tools, users are often unaware that their query is being shaped by a series of modifications and configurations outside their control. These hidden steps have a major impact on the AI's response and make it difficult to replicate or properly understand.
1) System Prompts: Background instructions, sometimes several pages long, added by the AI service provider to every query, control AI behaviour and priorities.
2) Query Modification: User inputs are often rewritten or wrapped in a template created by the AI service provider with the objective to guide or sanitize the query.
3) Context Injection: External information may be added to the query based on automated searches of document databases without disclosing the passages included.
4) Configuration Settings: Parameters controlled by the AI service provider control many elements of the response, including creativity, length, and style.
5) AI Model Updates: AI model changes and updates may happen anytime, affecting subsequent responses in unpredictable and sometimes undesirable ways.
6) Output Controllers: The AI response may be edited by automated tools before the response is returned to the user.
The Problems with Opaque AI Systems
For professionals relying on AI for critical tasks, the hidden processes in generative AI systems present significant challenges. These invisible steps mean that AI outputs cannot be understood or reliably reproduced by users of these systems.
Lack of Trust - The opacity of prompt pipeline makes it difficult to trust that the AI response was the most appropriate response to the user's question.
Lack of Control - Users cannot control key factors influencing AI responses, limiting their ability to deploy AI systems in a systematic and predictable way.
Lack of an Audit Trail - Users looking to decipher he inputs leading to a particular AI response often have access only to the original queries and final responses, not the hidden intermediate inputs.
These challenges underscore the need for more transparent and controllable AI solutions, especially in fields where explainability and consistency are paramount.