What the data for this category looks like
Quality traceability data for PCB manufacturing comes from customer Gerber files, drill tape files, BOM files, CAM parsing records, equipment operating parameters, bath solution test results, AOI inspection reports, electrical testing records, and laboratory slicing reports. Data update frequency is per production batch, per shift, or continuous collection. Daily documents include engineering production instructions, drilling and lamination parameters, chemical solution analysis records, and others. Single-batch data includes fields such as drilling aperture (unit: mm), lamination temperature (unit: ℃), electrical test continuity value (unit: Ω), and defect point coordinates. Document structures primarily consist of parameter tables, inspection reports, and parsing logs. The core association logic is the wet process superposition of multi-layer graphics and holes.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Dispersed data sources spanning multiple system documents require multi-turn dialogue to associate different types of fields and reports to avoid information fragmentation. High-frequency batch-level updates require dialogue contexts to be isolated by production batch to prevent cross-batch data confusion. Diverse document structures require prompts to adapt to different formats of parameters and reports to accurately extract key information. The serious consequences of quality problems require multi-turn dialogue to cover full-process associated data to ensure analysis completeness.
This document is written based on FastGPT v4.17.0.
How to set the configuration
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Generate a unique chatId per production batch | Isolate dialogue data for different production batches to avoid cross-batch information confusion |
messages | Retain 15–25 recent dialogue records | Cover the full workflow of single-batch quality analysis, retain context from previous troubleshooting steps |
variables | Preset three variables: production batch number, defect type, and inspection point | Quickly associate Gerber parsing, drilling parameters, and other data for the corresponding batch |
| Maximum context | 800–1200 characters | Adapt to the information density of single-batch PCB quality data, avoid response delays caused by overly long context |
| Question optimization | Enabled | Optimize keyword matching accuracy for professional quality issues, improve retrieval efficiency |
Three common misconfigurations
- Issue: Unable to retrieve dialogue records for a specified production batch when calling interfaces related to
chatId. Cause: A uniquechatIdwas not generated per production batch, resulting in dialogue data from different batches being overwritten. - Issue: Returns empty results when adjusting the offset parameter when retrieving the dialogue record list. Cause: The offset parameter was mistakenly used as the starting record serial number, without understanding that it represents the number of previously returned records to skip.
- Issue: The AI’s abnormal analysis in multi-turn dialogue does not associate with the drilling parameters of the corresponding batch. Cause: The
variableswere not configured correctly, and the production batch number was not passed as a retrieval condition.
How to confirm the configuration is correct
- Initiate a single-batch quality traceability dialogue, check that the
chatIdmatches the production number of the batch, to confirm that data isolation is effective. - Call the dialogue record list retrieval interface, adjust the offset parameter, and verify that the number of returned results matches expectations.
- Enter a query that includes the production batch number, check that the AI’s reply associates with the AOI and electrical test report data for that batch.
- After enabling the question optimization function, enter a professional quality issue description, check that the AI can accurately extract the defect type and inspection point.
The values provided are common starting points and should be measured against the reader's own samples.