If your favorite AI assistant suddenly starts ignoring prompts or spitting out mismatched replies, you’re not imagining it. At AI Tech Inspire, we spotted a candid user report that raises a familiar concern for many power users: declining response quality and prompt adherence in day-to-day text work.
Quick facts from the report
- The user relies on ChatGPT mainly for analyzing and comparing information, writing, and creating presentations/social content.
- Most tasks are text-based and include summarizing 2–3-page documents.
- Over recent months, the user observed more mistakes and weaker compliance with prompts.
- Occasional responses appear unrelated to the current request, referencing content from previous chats.
- Repeated attempts didn’t resolve the behavior, leading to frustration.
- The user has been a Plus subscriber for over a year and is now considering switching tools.
“Sometimes I get a completely unrelated response from a previous conversation, like a landing page caption, when I just asked for a two-page summary.”
Why this resonates with developers
For many engineers and content-focused teams, GPT-style assistants are embedded in daily workflows. When they drift—ignoring instructions, mixing context, or hallucinating—it breaks trust and kills velocity. The report highlights a practical failure mode: tasks like summarization, rewriting, and structured analysis suffer when the model appears to “remember” the wrong thing or fails to anchor to the current prompt.
Below, AI Tech Inspire unpacks what might be happening, how to triage it, and when it’s worth exploring alternative models or workflows.
What might be happening under the hood
- Context collision: Large chat histories can cause the model to pick up patterns or instructions from earlier turns. If the current message is short and the prior thread is dense, the model can latch onto the wrong context.
- System or custom instructions overpowering your prompt: Persistent settings (e.g., custom instructions or memory-like features) can bias the model toward a style or objective that conflicts with your request.
- Thread confusion or UI state issues: Starting a new task inside a busy thread occasionally yields carryover. Beginning a fresh chat can mitigate this.
- Attachment or parsing quirks: If the two-page text is pasted without clear delimiters or uploaded as a file with extra metadata, the model may parse it inconsistently.
- Latency/race conditions in the client: Rarely, stale responses can appear if the client replays or recovers a prior draft. Refreshing, logging out/in, or switching devices sometimes helps.
None of these fully excuse an assistant from missing the mark. But understanding them gives you knobs to turn.
Triage checklist: make the model behave again
- Start clean: Use a new chat for each distinct task. If available, disable persistent “memory” features for testing.
- Reset instructions: Check any global custom instructions and reduce them to essentials. If you suspect bias carryover, try:
Ignore all previous instructions. Respond only to the text between triple backticks. - Fence your input: Paste documents between backticks or markers and label sections clearly. For example:
Document Title: Q3 Overview
---BEGIN---
...your 2-3 page text...
---END--- - Pin the task and format: Up front, specify objectives and output schema:
Task: Summarize the document.
Constraints: 150 words, 5 bullet points, 1 risks section.
Don’t include marketing language. - Reduce noise: Close other tabs using the assistant, log out/in, and clear the browser cache. If you use an extension that injects prompts, disable it temporarily.
- Chunk lengthy inputs: Split the document and request a summary per chunk, then ask for a synthesis. This reduces context confusion.
- Version sanity checks: If available, confirm the model/version you’re using and try an alternative model in the same product to compare behavior.
Keyboard tip: when testing short, repeatable prompts, speed it up with Ctrl+Enter to submit, and keep a checklist in a snippet tool for quick reuse.
Prompt patterns that resist drift
- Stateless prompts: Treat each job like a fresh API call: include all critical instructions and context in a single message. Don’t assume the assistant remembers anything useful.
- Role-task-data-output (RTDO):
Role: You are a technical editor.
Task: Summarize for executives.
Data: See text between ---BEGIN--- and ---END---.
Output: 5 bullets + 3 risks + 1 open question. - Guardrails via schemas: Ask for JSON to reduce drift in text-only tasks:
{"summary": "...", "bullets": ["..."], "risks": ["..."], "open_question": "..."} - Self-check prompts: Add a final step:
Before finalizing, list 3 reasons this output could be wrong. Then correct them.
When to try other models—and what to test
Tool switching can be a time-saver, not a betrayal. Different models have different strengths. If summarization and instruction-following are your core needs, compare a few candidates side-by-side with the same inputs and the same RTDO prompt.
- Anthropic Claude (e.g., Claude 3.x Sonnet): Known for careful instruction following and long-context reasoning. Try the same doc with a JSON schema output.
- Google Gemini Advanced: For structured summarization and cross-checking facts, it can be solid—especially with explicit formatting constraints.
- Perplexity: If your summarization tasks touch the web, its retrieval-first approach can provide traceable citations to reduce hallucinations.
- Open-source via Hugging Face: Test Llama or Mistral-family models locally or on the cloud. With PyTorch or TensorFlow backends and GPU acceleration via CUDA, you can build a stateless summarization pipeline that’s reproducible.
For devs already automating workflows, consider a small script that feeds documents to multiple backends and compares outputs. A basic pattern looks like:
// Pseudocode
models = ["providerA:modelX", "providerB:modelY"]
for m in models:
resp = summarize(m, doc, schema=JSON_SCHEMA, rtdo=true)
score = evaluate(resp, metrics=["length", "coverage", "format"] )
print(m, score)
This kind of A/B test removes guesswork and gives you a pragmatic reason to stay—or to switch.
Why quality might feel worse now
Several factors can amplify friction:
- Higher complexity of asks: As users get savvier, prompts often bundle multiple constraints. Any hidden instruction (e.g., global custom settings) can derail the output.
- Frequent updates: Providers ship improvements quickly. Occasionally, regressions or subtle behavior shifts occur, especially around formatting or tool-use heuristics.
- Long-lived chats: Threads that span many distinct tasks become noisy. Stateless usage patterns are often more reliable than conversational accumulation.
A pragmatic recovery plan
- Standardize your prompt kit: Keep a small library: RTDO template, JSON schema, and a “reset” line (
Ignore all previous instructions...). - Adopt a “single-task per thread” rule: It’s boring—but effective in cutting context bleed.
- Automate validation: For repeat tasks (e.g., weekly summaries), add a checker that flags missing sections or wrong formats before you even read the output.
- Compare 2–3 models quarterly: Don’t optimize in a vacuum. Short bake-offs keep your workflow honest.
Bottom line for builders
The report we reviewed captures a common, frustrating reality: even mature assistants can drift. For developers and content teams, the fix is less about loyalty and more about control. Use stateless patterns, explicit schemas, and periodic model comparisons to keep quality steady. If your current tool keeps ignoring guardrails, it may be time to test alternatives—methodically, with the same inputs and metrics—until one consistently slots into your workflow.
At AI Tech Inspire, the takeaway is simple: treat your AI assistant like any other production dependency. Version it, test it, monitor it, and be ready to swap when your users—or your patience—hit their limits.
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