Multi-turn Dialogue and Prompt Engineering for Game Industry

What the Data for This Category Looks Like

Game investment research data comes from multiple public channels: quarterly financial reports of game manufacturers, official game license publication lists released by the regulatory authority, player community comments and rating data, real-time heat data from live streaming platforms, in-game economic operation reports, and more.

Update frequencies vary significantly: license information updates quarterly, financial reports are released quarterly or annually, player comments and live heat data update in real time, and in-game operation data updates hourly or daily.

Three types of document structures are supported: structured report documents (fields include game name, license number, launch date, revenue amount), semi-structured research report PDFs, and unstructured long-form community comment texts.

Constraints on Multi-turn Dialogue and Prompt Engineering

The varying update frequencies of multi-source data require dynamic adjustment of the recall time range in multi-turn dialogue, to avoid retrieving outdated license information or expired real-time heat data. The mixed structured and unstructured data format requires prompts to first identify the data type of a user’s question, then invoke structured retrieval and unstructured parsing processes separately. Diverse field units require prompts to unify output units to prevent confusion. Multi-turn dialogue context must retain parameters such as the user-specified game category and time range, so subsequent questions do not require repeated clarification.

How to Configure Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000–12000 characters`	Game investment research conversations need to retain multi-turn context including game names, time ranges and data types. This range prevents critical parameters from being lost due to context overflow.
`RECALL_TOP_K`	`Top 8–12 entries`	Game investment research data includes both structured reports and unstructured comments. Too many recalled entries introduce redundant content, while too few may miss key information.
`PROMPT_TEMPLATE`	`First extract the game name, time range and data type from the question, then recall corresponding knowledge base content, and finally unify units when outputting results`	Game investment research questions often include multi-dimensional parameters. Complete parameter extraction first before retrieval to ensure recalled content accurately matches requirements.
`PARSE_STRUCTURED_DATA`	`Enabled`	Game investment research contains large volumes of structured revenue, license and retention rate data. Enabling this setting allows direct extraction of field values, avoiding model misinterpretation of structured content.
`CONTEXT_SIMILARITY_THRESHOLD`	`0.75–0.85`	Semantic differences in game data are significant; revenue data for the same game varies noticeably across quarters. This threshold balances recall precision and coverage.

The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, 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

Issue: After uploading a file via the /v1/chat/completions API, the retrieved results do not include the uploaded file content. Cause: The request body does not include the collection_ids parameter to bind the target knowledge base, or the uploaded file format is not allowed for parsing via the PARSE_ALLOW_EXT configuration.
Issue: After the AI Chat node in a workflow runs, the global return result includes the node’s internal thought process and debug logs. Cause: The "Return Thought Process" setting of the node is not disabled, or the response_mode parameter is not specified to only return the final answer.
Issue: In multi-turn dialogue, subsequent questions cannot associate with the game name and time range specified in the previous round. Cause: The conversation context retention function is not enabled, or the maxContext value is smaller than the total character count of the conversation history, causing the context to be truncated.

How to Verify Proper Configuration

Upload a structured CSV file containing game revenue data, submit a question that includes the specified game name and corresponding time range, and verify whether the returned results include the corresponding data from the file.
Submit two consecutive questions: specify a specific game category and time range in the first question, then submit a second question requesting similar data, and verify whether the returned results automatically use the limiting conditions from the first round.
After configuring PROMPT_TEMPLATE, submit a question with ambiguous units, and verify whether the returned results uniformly label data units.
Call the conversation API, check whether the choices[0].message.content field in the response only includes the final answer content, with no additional intermediate 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.