What This Category of Data Looks Like
Semiconductor investment research draws data from public industry research reports, foundry public financial reports, semiconductor patent databases, device test reports, and EDA design output documents. Update cadences vary: industry reports are updated weekly or monthly, financial reports quarterly, patents in real time upon application submission, and mass production test reports updated alongside production cycles. Document structures are diverse: research reports include structured fields such as process nodes, yield rates, and revenue proportions. Test reports include standardized fields including test environment, units such as nanometers (nm), watts (W), and wafer diameter (inches). Some EDA output documents use structured JSON or CSV formats, while others are long-form deep reports spanning dozens of pages.
What Constraints Do These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
The multi-source nature, time-sensitive differences, specific unit requirements, and structural variations of semiconductor data create multiple constraints for multi-turn dialogue and prompt configuration. First, multi-turn dialogue must clearly label update times for different data sources, to avoid using expired parameters that skew investment research judgments. Second, built-in unit verification and conversion logic must be included, to standardize use of units such as nanometers (nm) and watts (W). This prevents investment research errors caused by parameter confusion. Third, adaptation must be made for documents of varying lengths. Long deep research reports require segmented retrieval to ensure complete information. Short test reports require precise field matching to improve extraction efficiency. Fourth, processing methods for structured financial report data and unstructured patent text must be differentiated. Logic such as parameter extraction and cross-manufacturer comparison is executed separately during multi-turn dialogue.
How to Configure the Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 10000–15000 characters | Adapts to the average length of semiconductor industry research reports and test reports, prevents context overflow in multi-turn dialogue |
parse_file_chunk_size | 800–1200 characters | Balances parameter extraction accuracy and retrieval efficiency, adapts to the structured parameter distribution of semiconductor documents |
recall_top_k | Top 8 entries | Covers core parameters such as process nodes and yield rates of major semiconductor manufacturers, meets investment research comparison needs |
similarity_threshold | 0.75–0.85 | Adapts to the accuracy requirements of semiconductor parameters, avoids introducing irrelevant test data or missing relevant documents |
rag_reference_format | [Document Source] Field: Parameter Value (Unit) | Clearly displays the source, field, and unit of semiconductor parameters, facilitating verification by investment research personnel |
workflow_hide_output | Enabled | Hides intermediate AI dialogue outputs, only retains unified replies processed by the text splicing component |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material formats, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: The annotation function in dialogue logs only covers text fragments, and does not associate specific fields and units of semiconductor parameters. Cause: The preset prompt does not explicitly require labeling the document fields, units, and sources corresponding to the parameters, resulting in annotations that cannot support the verification needs of investment research personnel.
- Phenomenon: Intermediate outputs of AI dialogue in the workflow are directly displayed on the page, without unified output via the text splicing component. Cause: The
workflow_hide_outputconfiguration is not enabled, or output trigger rules are not configured in the workflow, leading to exposure of intermediate AI content. - Phenomenon: The second AI node in multi-turn dialogue cannot read the output parameters of the first node. Cause: No variable transfer logic is configured in the workflow, and the output result of the first dialogue is not bound as the input context for the second dialogue, resulting in failure to transfer parameters across nodes.
How to Verify Correct Configuration
- Upload a semiconductor test report and industry research report, trigger multi-turn dialogue, verify that the context window covers all core parameters without overflow or truncation.
- Initiate multi-turn dialogue involving cross-manufacturer parameter comparison, verify that the AI output labels the units, sources, and update times of parameters.
- After configuring workflow hidden output, verify that the page only displays unified replies processed by the text splicing component, with no intermediate AI outputs.
- Trigger the knowledge base retrieval and AI dialogue process, verify that the AI-retrieved references include complete fields, parameter values, and unit information.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.