Design an NL-to-formula assistant
Design an NL-to-Formula Assistant
Problem Description
Design an intelligent assistant capable of converting natural language requests into executable spreadsheet-style formulas. This system is intended for a no-code table product, similar to Airtable or Google Sheets.
User Interaction & Output
Users will input requests in natural language, such as:
- “If
Statusis "Won" andAmount> 1000, returnAmount* 0.1 else 0.” - “Extract the domain from the
Emailfield.” - “Create a formula to label rows as "Late" if
Due Dateis before today andCompletedis false.”
The system's output should consist of:
- A proposed formula, expressed in the product's specific formula language.
- A concise explanation of the generated formula.
- Optionally, a few test cases or example evaluations to demonstrate the formula's behavior.
Key Requirements
- Ambiguity Resolution: The system must be able to identify ambiguous requests and engage in clarifying questions with the user.
- Contextual Awareness: It must respect the current table schema, including field names, data types, and handling of missing values.
- Safety & Security: The system must prevent data leakage and avoid generating formulas that could lead to destructive actions.
- Performance: Achieve a P95 latency of less than 2-3 seconds for interactive usage.
- Quality & Iteration: Provide a comprehensive plan for evaluating the quality of generated formulas and for continuous iteration and improvement.
Discussion Areas
Candidates should be prepared to cover the following aspects in their design:
- The end-to-end architecture, encompassing client-side interactions, orchestration logic, model calls, and supporting tools.
- Mechanisms for schema grounding and robust type checking.
- Strategies for handling ambiguity and facilitating multi-turn refinement conversations.
- Methods for validating generated formulas, including both static and dynamic checks.
- Offline and online evaluation metrics, along with dataset considerations.
- Identification of potential failure modes (e.g., hallucinated fields or functions, incorrect logic for edge cases) and proposed mitigations.
AI interview prep
Core dilemma
Balancing the flexibility and expressiveness of natural language input with the strict syntax, semantics, and contextual constraints of a formula language, all while meeting interactive latency targets, ensuring safety, and providing a robust feedback loop for continuous improvement.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design an NL-to-formula assistant
Design an NL-to-Formula Assistant
Problem Description
Design an intelligent assistant capable of converting natural language requests into executable spreadsheet-style formulas. This system is intended for a no-code table product, similar to Airtable or Google Sheets.
User Interaction & Output
Users will input requests in natural language, such as:
- “If
Statusis "Won" andAmount> 1000, returnAmount* 0.1 else 0.” - “Extract the domain from the
Emailfield.” - “Create a formula to label rows as "Late" if
Due Dateis before today andCompletedis false.”
The system's output should consist of:
- A proposed formula, expressed in the product's specific formula language.
- A concise explanation of the generated formula.
- Optionally, a few test cases or example evaluations to demonstrate the formula's behavior.
Key Requirements
- Ambiguity Resolution: The system must be able to identify ambiguous requests and engage in clarifying questions with the user.
- Contextual Awareness: It must respect the current table schema, including field names, data types, and handling of missing values.
- Safety & Security: The system must prevent data leakage and avoid generating formulas that could lead to destructive actions.
- Performance: Achieve a P95 latency of less than 2-3 seconds for interactive usage.
- Quality & Iteration: Provide a comprehensive plan for evaluating the quality of generated formulas and for continuous iteration and improvement.
Discussion Areas
Candidates should be prepared to cover the following aspects in their design:
- The end-to-end architecture, encompassing client-side interactions, orchestration logic, model calls, and supporting tools.
- Mechanisms for schema grounding and robust type checking.
- Strategies for handling ambiguity and facilitating multi-turn refinement conversations.
- Methods for validating generated formulas, including both static and dynamic checks.
- Offline and online evaluation metrics, along with dataset considerations.
- Identification of potential failure modes (e.g., hallucinated fields or functions, incorrect logic for edge cases) and proposed mitigations.