Context and Token Management for Medical Affairs R&D

Data Characteristics in this Category

Medical affairs R&D documents originate from diverse sources. These include clinical trial protocols, investigator brochures, medical reports, drug labels, post-market safety data, and various regulatory guidelines. Document update frequencies vary; clinical trial documents may undergo frequent revisions during a trial, while drug labels or regulatory guidelines are relatively stable but are periodically updated based on regulatory requirements or new research.

Document structures are highly complex, often containing numerous nested tables, figures, abbreviations, and specialized terminology. Fields and units are highly standardized, such as dose units (mg, g), time units (days, weeks, months), biological indicator units (mmol/L, U/L), and statistical indicators (p-value, confidence interval). However, variations in expression still exist between different documents.

Constraints Imposed by these Characteristics on "Context and Token"

The complexity of medical affairs documents significantly constrains context and token processing. First, documents contain extensive specialized terminology and abbreviations. The model requires a sufficiently long context to correctly understand their meaning and avoid ambiguity. For example, "AE" could mean "adverse event" or "arterial embolism."

Second, data in nested tables and figures is highly interconnected. Extracting information requires maintaining a long context window to capture logical relationships across rows and columns. If the context is too short, the model may fail to correctly associate data points.

Third, regulatory guidelines have strict logic. Understanding a single sentence might depend on definitions from previous paragraphs or even chapters. This necessitates a context window capable of covering sufficiently long text segments.

Finally, differing update frequencies mean the knowledge base must flexibly handle updates from various documents and accurately retrieve the latest information, avoiding outdated context.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`800–1200 characters` (characters)	Medical documents have complex sentence structures and high information density. Longer segments help preserve context integrity and prevent critical information from being truncated.
`Chunk Overlap Length` (Segment Overlap Length)	`150–250 characters` (characters)	Ensures sufficient overlap between adjacent segments to handle cross-segment logical associations and referential relationships, especially when parsing tables or lists.
`Recall count` (Recall Count)	`Top 5–8 entries` (top 5–8 items)	Given the specialized and precise requirements of medical affairs documents, increasing the recall count appropriately enhances relevant information coverage and reduces the risk of missed recall.
`Similarity threshold` (Similarity Threshold)	`0.75–0.82`	A higher threshold helps filter out irrelevant recall results, improving recall quality, especially in scenarios with confusing specialized terminology or polysemous words.
`Rerank result count` (Reranked Return Count)	`Top 3 entries` (top 3 items)	After reranking, the most relevant information should be presented first. This prevents the model from being distracted by secondary information during generation, improving response accuracy.
`maxContext`	`Calibrate by actual measurement` (Calibrate by actual measurement)	Determine the maximum context length that covers core information through testing, based on the complexity of typical questions and document length in actual business scenarios.

Three Common Mistakes

Model-generated answers are short and incomplete, but the knowledge base recalls long original texts. This typically occurs when maxContext or Chunk size (Segment Length) is set too small, preventing the model from accessing sufficient context during generation.
When processing queries involving tabular data, the model fails to correctly associate data from different columns. This may be due to insufficient Chunk Overlap Length (Segment Overlap Length), causing tabular or cross-tabular context information to be fragmented during segmentation.
The system misunderstands certain specialized terms, leading to incorrect information generation. This might be related to the knowledge base not specially handling these specialized terms, for example, by not marking these phrases as non-splittable in fullTextTokens.

How to Confirm Correct Configuration

Select a medical affairs document with complex tables or multi-level logic. Ask questions involving cross-paragraph or cross-table associated information. Check if the model can accurately extract and integrate the information.
Ask questions about common abbreviations or polysemous words in the document. Observe if the model can provide correct explanations and applications based on the context.
Select several typical queries. In the FastGPT interface, view the recalled original segments. Verify if the recall count meets expectations and if the segment content fully covers the core information required by the question.
Monitor token usage in system logs. Confirm that the actual maxContext usage matches the expected setting, ensuring no frequent truncation due to an undersized configuration.

Note: The values provided are common starting points. Measure them against your own samples for optimal performance.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.