What Data Looks Like for This Category
Data sources for this category include steel coil labels and quality certificates, press and feeder operation data, mold management systems, MES systems, and coordinate measuring machine or fixture inspection reports. Data updates trigger per coil of material, per production batch, and each time a mold is changed. Daily associated documents include stamping work instructions, material utilization rate sheets, mold inspection sheets, first and last piece records, dimensional inspection reports, and mold change records. Covered fields include steel coil grade, press pressure parameters, mold clearance values, first piece dimensional deviation values, mold change time, and more. Units include MPa, mm, hours, and others.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
This category of data updates frequently and links to multiple types of heterogeneous documents. Multi-turn dialogue must limit context scope to avoid interference from redundant information. Parameter differences between distinct steel coil batches and post-mold-change setups are significant. Prompts must explicitly bind variables tied to the current production batch and mold information. Data fields cover multi-dimensional content including equipment parameters and inspection results. In multi-turn interactions, follow business logic to gradually guide the addition of key information. Avoid requesting too many fields at once, which can cause interaction lag. The volume of document data called in a single session is large. Limit the effective content length of the context window to ensure response speed matches real-time analysis needs in stamping workshops.
How to Configure Settings
The following configuration rules are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Bind current batch steel coil grade, mold number, mold change time | Data for this category updates dynamically with production batches and mold changes, requiring precise association with the current production context |
messages | Retain the most recent 3 rounds of interaction and the first system prompt | Stamping defect analysis must focus on current issues and historical key parameters. Excessively long context slows response speed |
chatId | Generate a unique identifier based on production shift + batch number | Distinguish dialogue contexts for different production batches to avoid mixing data across batches |
| Maximum context length | 800–1200 characters | The volume of document data linked to a single batch in this category is large. Limiting length ensures response efficiency for real-time analysis |
| Question optimization | Enable and limit the optimization scope to stamping quality terminology | Filter irrelevant expressions and focus on typical defect analysis scenarios such as cracking, wrinkling, and springback |
Three Common Misconfigurations
- Symptom: In specific quality issue scenarios, AI responses automatically truncate to within 200 tokens, even if a maximum reply limit of over 3000 tokens is configured. Cause: The output length for stamping defect analysis is not explicitly defined in the prompt, triggering the system's default short response protection rule.
- Symptom: After enabling input guidance and configuring a custom thesaurus, an error appears in the debug preview interface, and the input guidance has no actual effect. Cause: The custom thesaurus is not associated with the current stamping quality analysis application, or the thesaurus import format does not meet system requirements.
- Symptom: Retrieval and response latency for multi-turn dialogue exceeds the real-time analysis needs of the workshop. Cause: The effective content length of the context window is not limited, and the system calls excessive historical document data not tied to the current batch, reducing retrieval and processing efficiency.
How to Verify Proper Configuration
- Launch a simulated dialogue about a stamping cracking issue, and check if the response automatically associates the configured
variablesparameters, such as steel coil grade and mold number. - Adjust the maximum context length to 1500 characters, launch a multi-turn interaction, and confirm that the response speed meets expectations with no obvious lag.
- Test dialogues for different production batches, and check if
chatIdcorrectly distinguishes contexts across different shifts and batches, with no mixing of cross-batch data. - After configuring the question optimization rule, input a query containing non-stamping terminology, and confirm that the system automatically filters irrelevant content and focuses on quality analysis scenarios.
The values provided here are common starting points, and should be evaluated against the reader's own samples.