Multi-Turn Conversations and Prompts for Target Discovery

Data Characteristics

Target discovery regulation data primarily comes from internal R&D specifications, laboratory Standard Operating Procedures (SOPs), project management documents, and compliance audit reports. These documents are typically stored in PDF, Word, or Markdown formats, with varying degrees of structural organization. SOP documents often provide step-by-step operational guides, including detailed experimental procedures, reagent and consumable lists, instrument parameter settings, and data recording requirements. Project management documents cover project initiation, approval processes, risk assessments, and phase reports. Data update frequency is relatively low, occurring mainly during regulation revisions, technology updates, or changes in regulatory requirements. Documents frequently contain specialized terminology, abbreviations, and specific units of measurement, such as nM (nanomolar), μM (micromolar), mg/mL (milligrams per milliliter), hours, and ℃ (degrees Celsius). This requires high precision in semantic understanding.

Constraints on Multi-Turn Conversations and Prompts

The structural variability of target discovery regulation documents poses challenges for context understanding in multi-turn conversations. For example, step descriptions in SOPs are often interconnected. The dialogue system must accurately identify the process stage related to the user's question and retrieve relevant preceding or subsequent step information. Accurate recognition of specialized terminology and units of measurement directly impacts prompt construction quality; incorrect recognition can lead to misleading answers from the model. Furthermore, due to the low data update frequency, the system must ensure knowledge base stability and consistency, avoiding information confusion caused by document version differences. In multi-turn conversations, users may frequently switch focus, moving from an experimental step to a related risk assessment. This requires the system to have flexible context switching capabilities and to dynamically adjust the emphasis of prompts based on different query intentions to ensure the precision of retrieved content.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`Chunk size` (Segment Length)	`800–1200 characters`	Balances semantic completeness of long texts with retrieval efficiency, avoiding excessive truncation of key information.
`Recall count` (Retrieval Count)	`5–7 entries`	Covers multiple relevant knowledge points potentially involved in multi-turn conversations, improving answer comprehensiveness.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Balances retrieval precision and breadth, reduces interference from irrelevant information, and ensures relevance.
`Rerank result count` (Reranked Return Count)	`3 entries`	Further refines retrieval results, placing the most relevant document segments at the forefront to optimize model input.
`maxContext`	`3000 Tokens`	Provides a sufficient historical information window for multi-turn conversations, maintaining conversational coherence.
`temperature`	`0.3–0.5`	Ensures the accuracy and stability of generated answers, reduces unnecessary divergence, and aligns with the rigor required for regulatory Q&A.

Common Pitfalls

Symptom: A user asks for detailed steps of an experiment, but the system returns a project risk assessment document. Reason: The knowledge base segmentation strategy failed to maintain the integrity of SOP steps, or similarity calculation failed to distinguish between different types of document content.
Symptom: The user uses the unit "nM" in a question, but the system provides an inconsistent unit "μM" in the answer, or fails to understand the unit. Reason: Prompt engineering did not sufficiently emphasize the ability to recognize and convert specialized terminology and units of measurement, or the representation of relevant units in the knowledge base was inconsistent.
Symptom: When asking follow-up questions about a specific detail of a regulation, the system fails to link previous conversation context, leading to repetitive or off-topic answers. Reason: The maxContext parameter was set too low, failing to retain a sufficiently long conversation history, or the multi-turn conversation context management mechanism failed.

Validation Steps

Select typical SOPs, project documents, and compliance files from target discovery regulations. Design complex multi-turn Q&A scenarios covering specialized terminology, process steps, and parameter units for testing. Observe the accuracy and coherence of system responses.
For specific units of measurement included in documents (e.g., nM, mg/mL, ℃), construct clear queries to verify if the system can correctly identify, interpret, or use these units in its answers.
Simulate rapid context switching in user queries between different document types (e.g., from an SOP to a risk assessment). Check if the system can quickly adjust retrieved content and provide relevant answers without losing context. Compare performance with different Recall count (Retrieval Count) and Similarity threshold (Similarity Threshold) settings.

The values provided are common starting points and should be measured against the reader's own samples.

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.