--- title: "Getting the most from AI assistants (Grok, ChatGPT, Claude and similar)" license: CC-BY-4.0 license_url: https://creativecommons.org/licenses/by/4.0/ source: https://iinventedlight.com/library/notes/getting-the-most-from-ai-assistants.md author: "iinventedlight (synthesis of cited sources); text drafted with AI assistance" date: 2026-10-14 status: reviewed reviewed_on: 2026-10-14 checked_on: 2026-10-14 sources: - id: S1 title: "How I use LLMs (Andrej Karpathy, 2025)" publisher: Andrej Karpathy (YouTube) url: https://www.youtube.com/watch?v=EWvNQjAaOHw accessed: 2026-10-14 license: "restricted: restated, not copied" - id: S2 title: "Sam Altman on Building the Future of AI (OpenAI Forum, 2026)" publisher: OpenAI (YouTube) url: https://www.youtube.com/watch?v=ZpUKNYcgM-E accessed: 2026-10-14 license: "restricted: restated, not copied" - id: S3 title: "Andrej Karpathy: Software Is Changing (Again) (2025)" publisher: Y Combinator (YouTube) url: https://www.youtube.com/watch?v=LCEmiRjPEtQ accessed: 2026-10-14 license: "restricted: restated, not copied" - id: S4 title: "Andrej Karpathy: From Vibe Coding to Agentic Engineering (2026)" publisher: Sequoia Capital (YouTube) url: https://www.youtube.com/watch?v=96jN2OCOfLs accessed: 2026-10-14 license: "restricted: restated, not copied" - id: S5 title: "How AI Impacts Skill Formation (arXiv 2601.20245, v2)" publisher: arXiv (Shen & Tamkin) url: https://arxiv.org/abs/2601.20245 accessed: 2026-10-14 license: "restricted: CC BY-NC-ND 4.0, restated, not copied" - id: S6 title: "Recursive Language Models (arXiv 2512.24601, v3)" publisher: arXiv (Zhang, Kraska, Khattab) url: https://arxiv.org/abs/2512.24601 accessed: 2026-10-14 license: CC BY 4.0 --- # Getting the most from AI assistants (Grok, ChatGPT, Claude and similar) *Note, 2026-10-14. Status: reviewed. Tags: ai, agents, research.* *Authorship: synthesis by iinventedlight from the cited sources; text drafted with AI assistance (Grok Bot), reviewed by Cole Withington on 2026-10-14.* ## Summary To get more from an assistant: pick the right mode (thinking, search or deep research, code tools) for the task [F2–F4]. Keep one topic per chat and paste in the material you want used [F1]. Ask for the sources behind factual claims and open them [F5, F6]. Treat confident answers and viral claims about AI with skepticism [F7, F8]. If you are learning, ask for explanations, not just answers [F9]. ## Sections ### Pick the right mode - Use a reasoning ("thinking") model for hard math, code and analysis; a quick model is enough for simple lookups [F2]. - Use web search or deep research for current facts, and a code tool for calculations [F3]. - Casual users who stick to default settings tend to underestimate what current models can do [F4]. ### Manage context - Start a new chat when you change topic, and put the documents you want used directly into the chat [F1]. - Save repeated instructions as custom instructions or a reusable assistant [F10]. - Very long inputs are hard for models. Research methods exist, but a plain chat may miss details in huge pastes [F11]. ### Ask for synthesis, not summary (our recommendation) - **Unsourced practice, not a tested finding:** ask the assistant to compare sources, find where they agree and disagree, and say what follows, rather than condensing one text. Ask it to mark what it inferred versus what a source states. None of our sources test this specific prompt style. ### Name sources and check claims - Ask for sources by name (paper, page, official video) and open the ones that matter. Models can hallucinate, especially without search [F5, F6]. - Be suspicious and probe. Models can be strong at hard tasks yet fail simple ones ("jagged") [F7]. - Check viral claims about AI against the original. Our own checks found social posts that overstated papers [F8]. ### Protect your own understanding - If the goal is learning, ask for explanations, or generate code and then ask the assistant to explain it. Full delegation went with the lowest learning scores in one study [F9]. ### Hype labels - **Dated:** product and model names in 2025 talks are out of date. The habits still apply; the menus may differ. - **Speculation:** claims that a single prompting trick "replaces" skills or that you "no longer need prompts" are unsupported. One such caption attributed to Altman does not appear in the talk's transcript. ## Facts - id: F1 claim: Karpathy explains that everything in a chat shares one context window, the model's working memory, and advises starting new chats for new topics because long contexts can distract the model and cost more. confidence: low date: 2025-02-27 sources: [S1] url: https://www.youtube.com/watch?v=EWvNQjAaOHw checked_on: 2026-10-14 license: CC BY 4.0 (our restatement) - id: F2 claim: Karpathy recommends reasoning ("thinking") models for hard math and code, and says they are not needed for simple lookups. confidence: low date: 2025-02-27 sources: [S1] url: https://www.youtube.com/watch?v=EWvNQjAaOHw checked_on: 2026-10-14 license: CC BY 4.0 (our restatement) - id: F3 claim: Karpathy recommends web search for fresh facts, deep research for long sourced reports, and a Python tool for calculations and charts. confidence: low date: 2025-02-27 sources: [S1] url: https://www.youtube.com/watch?v=EWvNQjAaOHw checked_on: 2026-10-14 license: CC BY 4.0 (our restatement) - id: F4 claim: Altman describes a "capability overhang". Casual users check in occasionally on default settings, never try reasoning modes, and so underestimate progress. confidence: low date: 2026-04-07 sources: [S2] url: https://www.youtube.com/watch?v=ZpUKNYcgM-E checked_on: 2026-10-14 license: CC BY 4.0 (our restatement) note: Speaker leads an AI company (conflict of interest). - id: F5 claim: Karpathy notes that a model's knowledge stops at a training cutoff and that answers without search risk hallucination. confidence: low date: 2025-02-27 sources: [S1] url: https://www.youtube.com/watch?v=EWvNQjAaOHw checked_on: 2026-10-14 license: CC BY 4.0 (our restatement) - id: F6 claim: Karpathy describes LLMs as able to hallucinate, having jagged abilities, keeping no lasting memory between sessions, and being vulnerable to prompt injection. confidence: low date: 2025-06-19 sources: [S3] url: https://www.youtube.com/watch?v=LCEmiRjPEtQ checked_on: 2026-10-14 license: CC BY 4.0 (our restatement) - id: F7 claim: Karpathy gives the example of a model that refactors large codebases yet gives absurd advice on a simple everyday question, and advises users to stay suspicious and probe the model. confidence: low date: 2026-04-29 sources: [S4] url: https://www.youtube.com/watch?v=96jN2OCOfLs checked_on: 2026-10-14 license: CC BY 4.0 (our restatement) note: S3 and S4 are the same speaker; jaggedness is not independently corroborated here. - id: F8 claim: A widely shared post said Recursive Language Models "solved AI memory". The paper makes no such claim; it evaluates an inference-time method on four benchmarks and lists open limitations. confidence: medium date: 2025-12-31 sources: [S6] url: https://arxiv.org/abs/2512.24601 checked_on: 2026-10-14 license: CC BY 4.0 - id: F9 claim: In a randomized study (n=52), learners using a chat AI scored about 17% lower on a later no-AI quiz. Those who generated code and then asked for explanations scored highest (about 86%), and those who fully delegated scored lowest (below 40%). confidence: low date: 2026-01-28 sources: [S5] url: https://arxiv.org/abs/2601.20245 checked_on: 2026-10-14 license: CC BY 4.0 (our restatement; source is CC BY-NC-ND, not quoted) note: Small, post-hoc clusters; a single one-hour task. - id: F10 claim: Karpathy recommends memory, custom instructions and custom assistants so you don't have to repeat the same prompts. confidence: low date: 2025-02-27 sources: [S1] url: https://www.youtube.com/watch?v=EWvNQjAaOHw checked_on: 2026-10-14 license: CC BY 4.0 (our restatement) - id: F11 claim: The Recursive Language Models paper reports that on information-dense tasks, summarizing or compacting long inputs drops needed details. On a pairwise-comparison task, base models scored F1 of 0.1% or less. confidence: medium date: 2025-12-31 sources: [S6] url: https://arxiv.org/abs/2512.24601 checked_on: 2026-10-14 license: CC BY 4.0 ## Pointers - answers: Screen-recorded walkthrough of everyday LLM features (search, deep research, code, voice, images) where: https://www.youtube.com/watch?v=EWvNQjAaOHw access: free on YouTube (2h11m; Feb 2025, so products are dated) ## Disagreements - None recorded. Note that most advice here comes from one practitioner (Karpathy). Independent open sources testing these habits are still needed before any fact is rated high.