EDA Tool Documentation Assistant

Semiconductor GenAI case study to accelerate onboarding and reduce documentation-heavy engineering delays.

About Customer

The customer is a semiconductor design organization onboarding engineers onto advanced EDA workflows across multiple projects. Tool documentation was large, fragmented, and highly technical, making ramp-up slow and expert dependent.

Leadership wanted a self-service technical assistant that could reduce onboarding friction without compromising response quality.

Customer EDA Documentation Context Horizontal swimlanes: sources flow into engineer tasks, then into business impact Documentation Sources Tool Manuals Vendor docs and command behavior Workflow Tutorials Project-specific execution guidance Internal Notes and Tips Known pitfalls and best practices Engineer Onboarding Work Asks setup, constraint, and debug questions Manual lookup across multiple sources Answers are slow unless experts are available Operational Impact High expert dependency Repeated escalations to seniors Slower onboarding velocity Ramp-up stretches from days to weeks More non-productive lookup time Less time on high-value engineering work

Problem Statement

New design engineers spent significant time searching EDA manuals and internal notes before they could perform productive work. Most questions still ended up with senior experts, creating a bottleneck in both learning and execution.

As project timelines tightened, documentation lookup overhead translated into delayed tool adoption and slower project starts.

  • Massive, complex documentation created a steep learning curve.
  • Engineers relied on internal experts for repetitive tool queries.
  • Onboarding cycles stretched from days into weeks.

Solution Architecture

Zettabolt deployed a ChatGPT-like assistant grounded on the customer's EDA (Electronic Design Automation) tool documentation. ZettaLens custom pipelines convert manuals, tutorials, and command references into AI-searchable knowledge - so chip-design engineers can ask complex tool questions in natural language and receive instant, source-cited answers. Onboarding shrinks from weeks to days, achieving 5X faster user onboarding and 100% self-service knowledge access, with a major drop in non-productive lookup time. Here is how we integrated the pipeline:

EDA Tool Documentation Assistant Chip Engineer How to set up timing constraint? EDA DocsZettaLens Vectorized AI LLM + RAGcreate_clock -name CLK \ -period 10 [get_ports clk]set_input_delay 2.0 \ -clock CLK [all_inputs]Ref: timing_guide.pdf p.18Instant + Cited Answer

Implementation Highlights

  • Built a ChatGPT-like assistant using LLM + RAG for EDA documentation support.
  • Used ZettaLens pipelines to create high-fidelity embeddings for technical content.
  • Enabled contextual question answering with source-grounded responses.

Implementation context: The solution started with high-volume onboarding questions and workflow-critical documentation sections. This focused rollout quickly reduced repetitive expert queries and gave new engineers reliable, self-serve access to tool knowledge from day one.

LLM RAG Semantic Search ZettaLens Pipelines EDA Onboarding

Business Impact

5X faster user onboarding
Massive reduction in non-productive lookup time
100% self-service knowledge access
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