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ChatGPT doesn’t actually work on tasks in the background. When it says it’s “still working” or “almost done,” it’s not like a human actually making progress behind the scenes. It generates responses in real time, meaning every time you ask for an update, it’s just coming up with a new response on the spot rather than tracking real progress.
This is a common issue when asking ChatGPT to handle long and complex tasks, especially when sources and citations are involved. It doesn’t have the ability to keep a persistent workflow like a human researcher would.
For a task like updating a 39-page speech with citations, a better tool would be ChatGPT DeepResearch, which is specifically designed for detailed, source-backed research. It can actually find and track sources, rather than just “hallucinating” progress like ChatGPT has been doing.
ETA: Here is DeepResarch results after 2 minutes...
Why ChatGPT Struggles with Long, Research-Heavy Tasks
For a task like updating a 39-page speech with citations, ChatGPT faces significant limitations. The core issue is that ChatGPT does not actually perform work in the background or maintain a persistent task state between responses. When it says things like “still working” or “almost done,” it’s merely generating plausible text in real-time rather than reporting genuine incremental progress. In fact, once ChatGPT finishes a response, it does no further processing until the user prompts it again
. This means:
- No True Background Processing: ChatGPT isn’t carrying out research or edits during the waiting time; it only pretends to, based on patterns in its training data
. There’s no continuous work being done in the minutes or hours after it replies – the model isn’t actually fetching new information unless instructed with a new prompt.
- Unreliable Time Estimates: Any projected timelines or “progress updates” it gives are not based on actual work. For example, users have seen GPT-4 respond with statements like it will “provide a comprehensive update within a few days,” but this is just an emulation of human-like behavior, not a real scheduled task
. The AI has no ability to wake itself later and continue; it simply stops until prompted again.
- Limited Context and Persistence: Handling a lengthy 39-page document is difficult because ChatGPT has a fixed memory window (context length). It might not fit the entire speech in one go, forcing the user to split the text into chunks. Each new user prompt risks losing some context from earlier parts of the speech. ChatGPT does remember conversation history to an extent, but it can easily forget or mix details when the content is very long or complex. There’s no built-in mechanism for it to persistently track changes across a large document over many turns.
- No Built-in Source Verification: By default, ChatGPT doesn’t provide citations or even reveal where information comes from
. It often cannot properly reference external sources because it wasn’t explicitly designed to link its statements to specific documents or URLs in a reliable way
. Even when asked to cite, ChatGPT can produce incomplete or incorrect references
. This lack of grounding makes it hard to trust for research-heavy updates – it might introduce factual errors or hallucinate sources.
In short, ChatGPT excels at generating fluent text quickly, but it struggles with extensive, detail-oriented editing tasks that require continual research and factual accuracy. It will generate an answer on demand, but it won’t methodically work through a long document with careful revisions and citations over time. These shortcomings set the stage for why a different approach – using DeepResearch – is better suited for the job.
DeepResearch: Purpose-Built for In-Depth, Source-Backed Research
DeepResearch is an AI-powered research agent specifically designed to overcome the above limitations. Unlike ChatGPT’s quick, single-turn answers, DeepResearch conducts in-depth, multi-step investigations and produces a structured, citation-backed report as the output
. In essence, it behaves more like a diligent analyst than a fast conversationalist. Key characteristics of DeepResearch include:
- Autonomous Multi-Step Process: DeepResearch can independently run for an extended period (e.g. 5–30 minutes) to gather and synthesize information
. You give it the prompt or task, and it will then browse the web, retrieve data from numerous sources, and iteratively refine the information without constant user intervention. This autonomy is crucial for complex tasks like updating a lengthy speech – the AI can methodically work through the content instead of relying on the user to prompt every small step.
- Integration of Multiple Sources: The agent is built to analyze hundreds of online sources (web pages, articles, studies, etc.) and even handle various data formats such as text, PDFs, and images
. It doesn’t rely solely on a pre-trained knowledge cutoff. For example, if the speech mentions statistics or events, DeepResearch will actively search for the latest data or news on those points. This means the updates it provides are grounded in current, real information from the web, rather than just whatever was in its training data.
- Structured Output with Citations: A hallmark of DeepResearch is that the results come as a well-organized report with clear citations to sources
. Every key fact or figure it adds to the speech can be backed by a reference. Instead of just stating “According to recent studies, X…”, it will include a citation like a footnote (as we see in this interface with the【source†lines】 format). This practice of citing sources makes it far easier to verify the information and is essential for maintaining credibility in the updated speech.
- Advanced Reasoning and Context Management: DeepResearch uses an advanced model (built on OpenAI’s upcoming o3 model) optimized for reasoning and analysis
. It can handle long documents by ingesting the entire speech (for instance, you could provide the 39-page speech as a PDF or text file) and maintains contextual understanding of that document throughout the research process
. In practical terms, it remembers what the speech is about, the key themes and arguments, and what sections might need updates. It adapts its research strategy based on this context – a capability far beyond standard ChatGPT
. This helps ensure that any new information it finds is relevant to the speech’s content.
In summary, DeepResearch is like a specialized research assistant: it diligently gathers information from many sources, keeps track of where facts come from, and focuses on producing a coherent, source-backed update. This makes it ideally suited to the task of updating a lengthy speech with accurate citations.
How DeepResearch Would Tackle Updating the 39-Page Speech
To illustrate the effectiveness of DeepResearch, let’s break down how it could efficiently update the speech with relevant, cited information while preserving the speech’s original intent. The process might look like this:
- Input and Analysis of the Original Speech: The entire 39-page speech can be provided to DeepResearch (as text or a PDF/Word document). The tool is capable of processing large documents and extracting their content
. It would read through the speech to understand its main themes, arguments, and the context of each section. Importantly, it identifies areas that likely need updating or fact-checking. For example, any statistics, dates, or references to policies and events might be flagged for verification.
- Underlying advantage: DeepResearch’s contextual understanding means it knows what the speech is arguing and can keep that in mind. It’s not going to treat the speech as unrelated fragments; it sees the overall narrative. This forms the basis for focused research, as it won’t randomly drift away from the speech’s topics.
- Identifying Information Gaps or Outdated Details: Next, DeepResearch would determine what information needs updating. Perhaps the speech was originally written a few years ago – economic figures, demographic data, or references to “recent” events could now be outdated. It might also find statements that are uncited or sourced from older research. DeepResearch will compile a list of specific queries to investigate. For example, if the speech says “In 2015, XYZ was the case…” and it’s now 2025, the tool might plan to check “XYZ statistics 2025 update” or find the latest research on that topic. This step is akin to making a research to-do list, ensuring all critical points will be addressed methodically.
- Autonomous Web Research for Each Point: With the list of needed updates, DeepResearch goes out to the web to gather information. It will perform searches for each item, navigate through relevant articles and data sources, and extract pertinent details. Crucially, it doesn’t stop at just one source – it cross-verifies facts by consulting multiple reputable sources
. For instance, if updating a statistic, it might pull the number from an official report or database and then double-check it against a news article or academic source for confirmation. During this process, the agent keeps track of exact source references (URLs, titles, etc.) for every piece of information it intends to use
. This ensures that by the end of its research, we not only have the needed facts but also a record of where they came from.
- Efficiency through automation: Unlike a human who might manually search and read dozens of webpages, DeepResearch can do this at scale quickly. It operates for up to half an hour continuously
, which suggests it systematically opens pages, scans for relevant content, and possibly even runs intermediate analysis (like summarizing a source or extracting a specific figure). The result is a breadth of information gathered in a relatively short time, all while the user doesn’t need to intervene.
4. Updating the Speech Content: Once the needed information is collected, DeepResearch will integrate the new material into the speech’s text. This involves editing or adding sentences and paragraphs where necessary to include the updated facts and figures. Each addition comes with a citation pointer. For example, if the original speech said “Our city’s population is growing rapidly,” and DeepResearch found the latest census data, it might revise this to: “Our city’s population grew by 10% over the last decade【source†lines】,” inserting the citation for the census report. The tool would do this for each point that required an update, carefully placing the information in context. It strives to match the tone and style of the existing speech so that the new portions blend seamlessly. If the speech was motivational or formal, the inserted lines will respect that style – the goal is to make the updates feel like part of the original writing, just with up-to-date evidence.
- Maintaining original intent: Because DeepResearch has kept the speech’s context in mind, it will ensure that any changes support the speech’s intent rather than derail it. For example, if the speech is advocating a certain policy, DeepResearch will bring in facts that reinforce the argument (assuming the facts support it) and avoid contradicting the speaker’s message. The reasoning ability of the agent helps here – it can infer which findings are relevant and how to incorporate them without changing the underlying message. Essentially, it “knows” what the speaker is trying to convey and acts in service of that narrative while updating details.
- Providing Proper Citations and References: After integrating new information, the output generated by DeepResearch will include citations in a clear format (such as footnotes or in-line references like the【source†lines】 style). Each citation corresponds to a credible source that was used. For instance, a claim in the speech now might be followed by a reference like “(World Bank, 2024)【source†lines】” indicating the source of that data. The agent compiles a bibliography or reference list as needed, ensuring traceability of every claim. This step is what transforms the speech into a research-backed document, lending greater authority to its content. It’s a stark improvement over ChatGPT’s typical outputs, which often lack sources or provide dubious ones. With DeepResearch, the user can verify any update by checking the cited material – a level of transparency critical for scholarly or professional work
.
6. Review and Refinement: Finally, DeepResearch would review the updated draft of the speech as a whole. This quality-check phase ensures that the flow of the speech remains logical and persuasive with the new information added. If some sections became too data-heavy or off-tone due to the updates, the agent can adjust phrasing to better align with the original intent. Because it keeps a contextual understanding of the entire document
, it can evaluate whether the additions might require rephrasing earlier or later parts for coherence. The end result delivered to the user is a revised 39-page speech where all factual assertions are up-to-date and backed by evidence. The structure and messaging of the speech remain intact, just strengthened by the inclusion of verified information.
Throughout this process, DeepResearch acts much like a dedicated researcher and editor combined. It does the heavy lifting of finding and vetting information, then rewrites parts of the speech to incorporate those findings, all while respecting the original speech’s purpose and style. For a human user, this dramatically reduces the effort required to update a long document – the tedious parts of research and citation gathering are handled by the AI.
Ensuring Accuracy and Alignment with the Speech’s Intent
One of the biggest advantages of using DeepResearch for this task is the increase in accuracy and credibility of the final product. Because the tool provides citations for each change, the updated speech can be trusted by the audience or stakeholders reviewing it. It’s not just claims anymore; it’s claims with evidence. Moreover, the way DeepResearch works inherently helps maintain alignment with the speech’s intent:
- Verified Information: By pulling facts from authoritative sources and citing them, DeepResearch minimizes the risk of introducing false or unsupported claims. The sources can be checked by the speechwriter or audience, which adds a layer of verification. This is a step-change from ChatGPT’s known tendency to sometimes invent plausible-sounding but incorrect info. In fact, generative AI models historically struggled to cite sources consistently
, but DeepResearch was designed to close that gap by making citations a core feature of its output
. The speech’s arguments are now backed by current data, making them stronger and more persuasive. For example, if the speech originally claimed “Project ABC will create thousands of jobs,” the updated version might specify “Project ABC created 5,000 jobs in 2024【source†lines】,” turning a generic claim into a concrete, verifiable statement.
- Consistency with Original Message: DeepResearch’s ability to maintain context
means it does not go off on tangents or add information that doesn’t serve the speech’s goals. Every piece of new content is added with the question in mind: “Does this support the point being made?” This aligns with the original intent. If a particular statistic turned out to contradict the speech’s stance, the tool’s output can be reviewed and that part can be handled carefully (in some cases, it might be better to omit an update if it undermines the message, or the speechwriter can adjust the narrative accordingly). Essentially, DeepResearch provides the facts, but it’s still up to the user to decide how to use them. The good news is that because the agent presents the facts in context, it’s easier to integrate or tweak them without rewriting the whole speech. The speech remains the same at its core – the updates are in service of clarity and accuracy, not changing the speaker’s opinions or calls-to-action.
- Structured and Readable Output: Since DeepResearch is geared towards producing reports for professionals, it tends to output information in a clear, organized manner
. When applied to a speech, this means the updated draft will be neatly formatted and easy to follow. Citations might appear as footnotes or bracketed references that don’t interrupt the flow for a live speech delivery (they can be kept on the printed version or slides). The language used for inserted content will match the original’s tone. The user likely won’t have to do heavy editing to correct the AI’s writing – at most, they might adjust a few sentences for personal style. This well-organized result saves time and ensures the speech is ready to go with minimal polishing.
In using DeepResearch, the speechwriter effectively gains a research assistant that ensures every significant statement in the speech is backed by current evidence. The final output is not only more informative but also more credible. Any claims that could be challenged now have the weight of sources behind them, and any outdated info has been refreshed. Yet the speech’s persuasive impact and original intent are preserved, because all changes were made in alignment with the speech’s existing narrative framework.
Conclusion: A Well-Researched, Citation-Backed Speech with DeepResearch
By leveraging DeepResearch for this extensive update, the user can transform their 39-page speech into a thoroughly research-backed document. The process addresses ChatGPT’s shortcomings by introducing true multi-step research, source verification, and persistence in handling a lengthy text. Instead of getting stalled or receiving generic assurances of progress, the user receives a tangible, updated speech complete with citations.
In practical terms, DeepResearch effectively turns a labor-intensive task (that could take a human many hours of reading and fact-checking) into an automated research cycle completed in a matter of minutes
. The result is a well-organized speech where every update is transparent (you see exactly where the info came from) and relevant (fit neatly into the speech’s context). The speech’s original goals and tone are maintained, but now it carries more authority because all claims are supported by up-to-date evidence.
Ultimately, the difference comes down to the design of the tool: ChatGPT is a generalist communicator, while DeepResearch is a specialist in deep-dive research. For a complex editing task like this, the specialized approach proves far more effective. It persists through the entire document, continuously referring to external information and back to the speech, until it produces a comprehensive update that aligns with what the speaker wants to convey. This ensures the updated speech is not only compelling in its rhetoric but also solid in its factual foundation – ready to withstand scrutiny and deliver maximum impact to its audience.
Sources:
- Reddit – Discussion on ChatGPT’s lack of background tasking
- The Atlantic – Analysis of generative AI struggles with accurate citations
- DataCamp Blog – OpenAI’s Deep Research introduction (multi-step research & structured reports)
- Campus Technology – New OpenAI “Deep Research” Agent (operates 5–30 min with web browsing, compiles reports with citations)
- Elite AI Tools – DeepResearch description (analyzes hundreds of sources, outputs clear citations, processes PDFs with context)
- Campus Technology – DeepResearch’s ability to accept PDFs/Docs and produce tailored responses
- Campus Technology – Note on accuracy and need for verification despite advanced research capabilities