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Bio, Work & Ideas

Jaspreet Singh

Conference affiliation: Intuit · 2025

Jaspreet Singh is a software engineer specializing in personalized financial AI that helps taxpayers understand refunds, deductions, and other consequential financial decisions. In 2025, he was a senior staff software engineer at Intuit and technical lead for the Smart TurboTax AI team. His earlier work on personalization and recommendation engines informed his approach to tailoring tax explanations to individual circumstances.

His TurboTax architecture combines Intuit’s GenOS platform, Claude and OpenAI models, and retrieval-augmented generation and GraphRAG to answer questions using proprietary tax knowledge and changing IRS requirements. Prepared explanations of refunds and deductions follow different workflows from open-ended questions about a customer’s situation.

  • Separate tax calculation from explanation. Established tax engines produce the numbers; language models explain them. Additional safeguards detect invented or altered figures before customers receive an answer.
  • Put tax experts inside the development process. Tax analysts write prompts, assess responses, and create reference examples for expert-grounded automated evaluation. Automated judges handle smaller changes, while substantial model updates or tax-law changes warrant renewed specialist review.
  • Treat latency and model changes as operational constraints. Singh tested fine-tuning Claude 3 Haiku to shorten prompts and reduce response times, while recognizing that specialization can limit flexibility. Peak filing demand, vendor lock-in, and difficult model upgrades make evaluation and product safeguards essential.

His priority is human-supervised tax explanations grounded in authoritative calculations, adapted to each taxpayer, and designed for the practical demands of regulated consumer software.

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TurboTax combines prepared prompts, retrieval, tax experts and layered evaluations to explain personal tax outcomes while keeping calculations in its tax engine.

  • Explain the tax outcome
    0:16 ↗
  • Shared infrastructure for a regulated product
    1:37 ↗
  • Prepared explanations and open-ended questions
    3:03 ↗
  • Tax experts write prompts and establish the baseline
    5:52 ↗
  • Fine-tuning to reduce the instruction burden
    7:25 ↗
  • Evaluate the answer and the evaluator
    8:29 ↗
  • Contracts and latency shape the product
    10:09 ↗
  • How much evaluation does a prompt change need?
    12:30 ↗
  • Route questions by what the user needs
    13:57 ↗
  • Keep calculated numbers separate from generated language
    15:13 ↗
  • Personalization matters beyond retrieval choice
    16:42 ↗
  • Assurance depends on the scope of the explanation
    17:49 ↗

References