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Fact check: My previous questions
Executive Summary
The materials you provided make three clear factual clusters: technical explanations that chat systems can maintain conversational context by carrying prior turns into subsequent inputs; platform-specific guidance showing how developers deliberately persist conversation state (ChatML, QnA Maker); and unrelated documentation about query-history tooling in data platforms and separate advice pieces about conversational follow-ups. The core claim that “ChatGPT retains context through prior questions” is supported by technical community and vendor guidance, but the mechanisms and limits differ by implementation and are not present in unrelated query-history references [1] [2] [3] [4] [5] [6] [7] [8] [9].
1. The technical community says: transformer models can carry context — here’s what that really means
Community explanations explain that ChatGPT and similar transformer-based models maintain a working “context” by receiving previous turns as part of a concatenated input sequence; this enables the model to reference earlier content within its maximum context window. This is a statement about input engineering, not magical memory: the model’s responses depend on the tokens it is given at each call. A 2022 Stack Exchange discussion captures this technical reality and frames it as an architecture-driven capability to process long input sequences, allowing conversation-like continuity so long as prior content is included in the prompt [1]. The discussion is informal community guidance but aligns with core transformer design principles and explains practical limits tied to sequence length and prompt framing [1].
2. Vendor guidance shows deliberate session-building patterns — developers are expected to manage history
Platform-level guidance provides concrete patterns for preserving conversation state across requests: the ChatGPT API forum recommends using ChatML or equivalent message arrays to append assistant and user turns into a running session, making the history explicit in each API call. This demonstrates that persistence of “context” is a developer-managed artifact of request construction rather than an opaque server-side memory [2]. The advice is dated March 2023 and reflects the API-era approach where every new response typically includes a message array representing prior exchanges so the model can use them as context [2]. That recommendation underscores trade-offs — longer histories cost more tokens and can hit model limits, which developers must weigh.
3. Microsoft’s QnA Maker documents multi-turn flows — enterprise systems codify follow-ups
Microsoft’s QnA Maker guidance (updated June 12, 2025) outlines multi-turn conversation design by storing follow-up prompts and mapping them to knowledge-base Q&A flows, enabling controlled conversational branching. This is not the same as a model retaining arbitrary prior user questions; instead, it’s a knowledge-engineering approach where the system author defines which prior turns matter and how the system should respond to them [3]. The documentation reflects an enterprise agenda: promote structured, auditable dialog paths that scale for production scenarios, privileging predictable continuity over open-ended memory.
4. Several documents in your set are unrelated to “previous questions” but clarify different history concepts
Reference materials from Databricks and Snowflake presented in your set focus on query history as operational telemetry, not conversational history. These resources explain how to inspect SQL query performance and audit job runs through system tables and UI surfaces, rather than retain or replay conversational turns [4] [5] [6]. Treating those artifacts as evidence for conversational context conflates distinct meanings of “history”: one is analytic metadata about system operations; the other is a crafted sequence of messages passed to a language model. Recognizing this difference prevents mistaken inference that database query logs imply model-level conversational memory.
5. Conversation-skills articles illustrate different aims — prompting good follow-ups versus technical context retention
The articles on follow-up questioning (2019–2025) offer communicative techniques — ask for elaboration, reframe questions, probe assumptions — and they do not claim models retain prior input independently of the prompt [7] [8] [9]. Their inclusion in your dataset signals a human-centric perspective on sustaining dialogue, not a technical justification for model memory. These pieces carry a different agenda: improve interviewer or conversationalist skill, not inform API design. Juxtaposed with the technical sources, they remind users that designing effective prompts and follow-ups often yields better outcomes than assuming persistent machine memory.