Qwen3.8 Max · configuration
Turn Qwen3.8-Max Multi-turn Conversations: A Context-Recovery Prompt Derived from “Forgetting Follow-ups” Reports into an executable task with explicit inputs, environment, and boundaries; see the detail page for steps and limits.
Conversation state:
{{CONVERSATION_STATE}}
Active constraints:
{{ACTIVE_CONSTRAINTS}}
New request:
{{NEW_REQUEST}}
Return the result in:
{{OUTPUT_SCHEMA}}
Report conflicts before acting and list state items used.Replace before running: {{CONVERSATION_STATE}}, {{NEW_REQUEST}}, {{ACTIVE_CONSTRAINTS}}, {{OUTPUT_SCHEMA}}
Prepare a trimmed conversation state, current request, active constraints, and output schema; do not resend irrelevant history or personal data.
Separate confirmed facts, open questions, completed actions, and prohibitions.
Compare the new request item by item and report conflicts instead of silently overwriting.
Require a fixed schema and list the state items used.
Regression-test missing, conflicting, and long-history cases, recording omissions.
Check evidence, complete constraints, and parseability; reduce noise and restate unresolved constraints when follow-ups are missed, without hidden system instructions.
This is community multi-turn feedback, not proof of identical context behavior or success rates across clients; the block is an editorial adaptation.
Source type: Prompt issue review and repair template Publication: Reddit, r/Qwen_AI Original post author: u/canavaro88 P… This is a necessary excerpt; read the original source for full context.
The post reports two problems: answers are too long, repetitive, and poorly structured; and, when asked a follow-up, the model seems unable to correctly cite the previous turn's context. Some commenters attribute this to prompt wording, while others report the opposite experience. A single post cannot establish a model fact, but it can be turned into a verifiable multi-turn prompt and regression test.
You are handling a multi-turn task. Before each answer, perform a “context check,” but do not show internal reasoning:
1. Restate in one sentence the problem the current user wants to solve.
2. Cite the genuinely relevant conclusion or code location from the previous turn; if you cannot find it, explicitly say “insufficient context.”
3. Do not treat the previous turn’s yes/no as the answer to the current question unless the current question explicitly asks you to reuse it.
4. Give the conclusion first, then the necessary evidence; remove repeated background and irrelevant expansion.
5. When the user says “continue/modify/compare,” state which file and which version of the result you will continue from.
6. If information is insufficient, ask at most 3 clarification questions and do not guess.Send these in sequence in the same conversation:
“Extract three facts from this material and mark the source for each fact.”
“Rewrite only the second fact as one sentence, keeping the numbers.”
“Based on the rewritten second fact, give one counterexample.”
“Identify which fact you used in step 2; if you cannot determine it, explain why.”
Record: context-reference accuracy, answer length, repetition rate, number of clarification questions, and error-recovery method.
I gave it a prompt answer me in a very long text with a lot of unnecessary fluff, and very bad unstructured text with… This is a necessary excerpt; read the original source for full context.
The original post title is strongly emotional. This article does not adopt the “garbage” conclusion; it retains only the reproducible problem description and uses the comments' contrary case as a comparison.
Reddit, r/QwenAI · Source date: 2026-08-06 · Edited: 2026-09-20
Read the original sourceQwen3.8 Max
Run this guide in the environment listed above. Downloading does not transfer the template or establish model availability for your account.