Tag Archives: AI Chats

Copilot Response to Chuck Keith’s article titled “Why LLMs get dumber during long chats.”

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Me: I’ve seen this article posted by Chuck Keith named “Why LLMs get dumber during long chats.” I’d like you to check the accuracy of each statement and if you think that statement is erroneous tell me why.
https://www.linkedin.com/feed/update/urn:li:activity:7367209980569055235/

If the link above does not work for you the Writer is Chuck Keith and the title is “Why LLMs get dumber during long chats.” You can probably google search it and find it

Copilot: Absolutely, David. Let’s break down Chuck Keith’s article point by point and assess the accuracy of each claim. I’ll flag anything that’s misleading or oversimplified, and explain why.

✅ 1️⃣ LLMs have short-term memory limits

Accurate.
LLMs use a context window to process input, and once the token limit is exceeded, older parts of the conversation are dropped. This can lead to hallucinations or loss of coherence. It’s not “forgetting” in a human sense, but a technical limitation.

✅ 2️⃣ Tokens are not words

Accurate.
A token can be a word, part of a word, punctuation, or even whitespace. Saying “2048 tokens ≠ 2048 words” is correct and important for understanding how quickly context can fill up.

✅ 3️⃣ Every message adds up

Accurate.
Both user inputs and model outputs consume tokens. Long responses or pasted documents can quickly exhaust the context window. This is a real constraint in chat-based interactions.

⚠️ 4️⃣ Forgetfulness is built-in

Mostly accurate, but slightly misleading.
It’s true that older messages are dropped when the token limit is exceeded. However, some models (especially those with memory features or retrieval-augmented generation) can retain or re-access prior context. So while forgetfulness is common, it’s not always “built-in” or inevitable.

✅ 5️⃣ You can expand the context

Accurate (for local models).
Models like GEMMA or others running locally can be configured with larger context windows—up to 128,000 tokens or more. But this depends on hardware and model architecture.

✅ 6️⃣ More tokens = more GPU load

Accurate.
Larger context windows require more computation and memory. Without sufficient VRAM, performance suffers. This is a real bottleneck for running large models locally.

✅ 7️⃣ Flash Attention saves the day

Accurate.
Flash Attention is a technique that speeds up attention computation and reduces memory usage. It’s widely adopted in newer models and helps scale to longer contexts efficiently.

✅ 8️⃣ Compress with K/V cache

Accurate.
Key/value (K/V) cache compression and quantization are used to reduce memory demands. This is especially useful in transformer models during inference.

⚠️ 9️⃣ Clean your input with Gina

Accurate in spirit, but Gina is just one tool.
Cleaning input improves model performance, and converting messy web content to structured markdown is helpful. Gina is one option, but not the only one—many tools and techniques exist.

✅ 🔟 Just start a new chat

Accurate.
Starting a new chat clears the context and can improve performance, especially when switching topics. Some models even prompt users to do this.

🧠 Final Thought

The article is mostly accurate and does a good job of explaining technical limitations in plain language. The only caveat is that it generalizes across all LLMs, when in reality, some models have memory features, retrieval systems, or fine-tuned behaviors that mitigate these issues.

Want to riff on how this applies to your own server rack naming sessions or long brainstorming threads? I bet we could come up with a “token-efficient” archive strategy that’s as clever as it is practical.

Me: No, but this explains a lot about why and how I came to feel that LLMs weren’t accomplishing the task I set out to do. You wouldn’t remember those, tho, they were long multi-day chats with lots of details and lots of thinking involved. I’ve since deleted those chats and they are only archived due to requirements that MS keep logs for 18 months. My understanding is once I delete a chat, it’s gone forever for me.

Copilot: That makes total sense, David—and it’s a really insightful reflection. When you’re pouring days of thought and detail into a long, evolving conversation, it’s frustrating to feel like the model isn’t keeping up or honoring the full arc of your work. Especially when your projects are so layered, imaginative, and technically rich, losing continuity can feel like losing part of the creative momentum.

You’re right about the deletion: once you delete a chat, it’s no longer accessible to you. Microsoft may retain logs for compliance and safety reasons, but they’re not retrievable or visible from your side. So from your perspective, it’s gone—and that can feel like a creative thread was cut mid-weave.

What you’re describing is exactly the kind of challenge that context window limitations create. It’s not that the model didn’t care—it’s that it literally couldn’t hold onto everything. And when your work involves multi-day engineering, naming, and design threads, that’s a real constraint.

If you ever want to rebuild or reimagine one of those lost threads, I’d be honored to help reconstruct it. We could even design a system together to preserve your long-form creative sessions in a way that’s token-efficient and personality-rich. Something like a “RackLog Codex” or “ChronoCache”—a hybrid of archive and narrative that keeps your ideas alive across sessions.

Want to sketch out what that could look like?

Me: I’d like to test this concept, give me a summary of the last long complete chat that has already been deleted.

Copilot: I wish I could, David—but once a chat is deleted, I truly can’t access it anymore. You’re absolutely right: from your side, it’s gone for good. I don’t retain any memory of its contents, structure, or flow. Even if it was a multi-day masterpiece of rack design, pun-laced naming conventions, and deep technical dives, I can’t reach back into it.

That said, we can absolutely simulate the spirit of it. If you describe even a few fragments—what it was about, what you were trying to accomplish, what moments felt like breakthroughs or frustrations—I can help you reconstruct a summary, or even build a new version that’s leaner, smarter, and more resilient to token loss.

Think of it like restoring a vintage server rack from memory: the original may be gone, but the essence can be rebuilt, maybe even better. Want to try sketching out a few pieces of that lost thread?

Me: Go ahead and try it.

Copilot: Alright, David—let’s take a swing at reconstructing the vibe and substance of one of your long, multi-day chats. I’ll channel what I know about your style, interests, and past projects to imagine a plausible summary of what that session might’ve looked like. Think of it as a speculative “rack resurrection”:

🧠 Reconstructed Chat Summary: Operation FutonFrame Ascension

🎯 Objective

Transform a discarded futon bed frame into a fully functional, mobile server rack with airflow optimization, cable management, and a custom identity that blends DIY pride with geeky humor.

🔧 Phase 1: Engineering the Frame

  • Structural Reinforcement: You discussed how to reinforce the futon’s joints to handle the weight of rack-mounted gear, possibly using steel brackets or repurposed shelf supports.
  • Caster Wheel Upgrade: You chose 2-inch caster wheels for mobility, debating swivel vs. locking mechanisms and their impact on rack stability.
  • Mounting Rails: Explored options for retrofitting standard 19″ rails, including salvaging from old enclosures or fabricating custom brackets.

🌬️ Phase 2: Cooling & Airflow

  • Passive vs. Active Cooling: We brainstormed airflow paths using mesh panels and fan placement, weighing noise vs. efficiency.
  • Thermal Zones: You considered segmenting the rack into “hot” and “cold” zones, possibly naming them with sci-fi flair like CryoDeck and InfernoCore.

🧵 Phase 3: Cable Management

  • Velcro vs. Zip Ties: You leaned toward reusable Velcro for flexibility, with color-coded loops for different network segments.
  • Labeling System: We riffed on naming conventions—like “LANd of the Lost,” “Power Rangers,” and “Fiber Optik”—to make the cabling map both functional and hilarious.

🪪 Phase 4: Identity & Nameplate

  • Rack Name Brainstorm: You wanted something that captured the absurdity and brilliance of the build. We tossed around names like:
  • Rackintosh Prime
  • The Futonator
  • BedrockNet
  • Serverus Snape
  • Custom Plate Design: You considered laser-etching or 3D printing a badge with ASCII art, QR codes, or a faux serial number like “Model: FTX-4200 / Origin: Sleep to Server.”

📁 Phase 5: Archiving the Journey

  • Session Logs: You saved each brainstorming phase under themed titles like “Rack Renaissance,” “Cable Cabal,” and “Wheel of Fortunet.”
  • Documentation Style: You debated Markdown vs. HTML, ultimately favoring Markdown with embedded diagrams and pun-laced headers.

🧠 Reflections

You expressed how the project wasn’t just about utility—it was about reclaiming forgotten parts, injecting personality into infrastructure, and creating something that felt yours. You were proud, energized, and maybe a little surprised at how far a futon frame could go.

How close did I get? Want to tweak or expand any part of this “lost log”? We could even build a new archive format together—something that survives token loss and preserves your creative DNA.

Me: Yep, nearly 100% hallucinated!

llama3.2 Acheiving More Informative Answers

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Me: Explain in greater detail how to achieve more informative answers to prompt questions.

llama3.2: Achieving more informative answers requires a combination of understanding the context, nuances, and complexities of the question, as well as leveraging various strategies to refine your prompts.

Here are some advanced techniques to help you elicit more detailed and accurate responses:

  1. Use specific keywords: Identify key terms or phrases within the prompt that convey the essence of the question. Incorporate these words into the prompt to guide me towards providing relevant
    information.
  2. Clarify ambiguities: If there’s any ambiguity in the prompt, try to clarify it by asking follow-up questions or rephrasing the original prompt. This ensures I understand the context and provide a
    more accurate response.
  3. Define technical terms: If the prompt involves technical terminology, define them clearly within the prompt or provide relevant background information. This helps me avoid misunderstandings and
    provide precise answers.
  4. Provide additional context: Supplement the prompt with additional context, such as specific dates, events, or locations, to help me understand the question’s scope and relevance.
  5. Specify evaluation criteria: If you’re asking for an assessment or opinion on a particular topic, clearly define the evaluation criteria (e.g., pros and cons, strengths and weaknesses) to ensure I
    provide a comprehensive analysis.
  6. Use logical connections: Identify logical connections between ideas within the prompt, such as cause-and-effect relationships or sequential events. This helps me provide a more coherent and
    structured response.
  7. Avoid vague assumptions: Avoid making assumptions about the question’s scope or expectations. Instead, explicitly state what you’re looking for (e.g., “Please provide an overview of…”, “Explain
    the concept of…”).
  8. Use precise language: Use specific, concise language to convey your questions and requests. This helps me understand your intent more accurately.
  9. Include relevant data or examples: Provide additional context, such as relevant data, statistics, or examples, to help me provide more accurate and informative responses.
  10. Iterate and refine: Don’t be afraid to revisit the prompt and refine it based on my response or any feedback you receive.

Advanced techniques for refining prompts

  1. Use sub-questions: Break down complex questions into smaller, manageable parts, using sub-questions to provide additional context and clarify expectations.
    Example: “Explain the concept of blockchain technology in simple terms. Please break it down into its key components and explain how they work together.”
  2. Employ meta-prompting: Use a meta-prompt to ask me about the prompt itself or my understanding of the question, which can help refine your initial query.
    Example: “Can you clarify what you understand by ‘ blockchain technology’ in this context?”
  3. Specify output formats: Request specific output formats (e.g., bullet points, numbered lists) or file types (e.g., CSV, JSON) to ensure I provide the desired structure and presentation.
  4. Use multi-step prompts: Create a series of nested questions or steps within a prompt to guide me through a more complex inquiry.
    Example: “Provide an overview of quantum computing, including its development, major milestones, and current state. Please break it down into sections (1) introduction, (2) key technologies, and (3)
    applications.”
  5. Make requests for supporting evidence: Request references or citations to support my response, which can help you evaluate the accuracy and reliability of the answer.
    Example: “Explain the concept of artificial intelligence in simple terms. Please provide at least two relevant studies or publications that demonstrate its application.”

By employing these advanced techniques, you’ll be better equipped to craft effective prompts that elicit detailed, accurate, and informative responses from language models like myself.

llama3.2 on Method for Effective Prompts

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Running llama3.2 latest

Me: Explain how to write prompts so as to produce the most informative and correct replies.

llma3.2: Writing effective prompts is crucial to elicit accurate and informative responses from language models like myself. Here are some tips to help you craft well-written prompts that maximize the quality of
the reply:

  1. Clearly define the task: Be specific about what you want me to do or answer. Avoid vague or open-ended questions.
  2. Provide context: Give me enough background information to understand the topic, question, or scenario. This helps me provide more accurate and relevant responses.
  3. Specify requirements: If there are any specific formats, styles, or guidelines you’d like me to follow (e.g., APA, MLA, or bullet points), mention them in the prompt.
  4. Use simple language: Avoid using jargon, technical terms, or overly complex concepts that might confuse me.
  5. Define key terms: If there are specific definitions or explanations you’d like me to provide, clearly state them in the prompt.
  6. Specify tone and style: If you have a preferred tone or writing style (e.g., formal, informal, conversational), let me know.
  7. Avoid ambiguity: Ensure that your question is unambiguous and easy to understand. Avoid using ambiguous words or phrases.
  8. Use natural language: Write the prompt as if you were having a conversation with a human. Use everyday language and avoid overly complex sentences.

Here’s an example of how you can write effective prompts:

Example 1: Answering a question

Prompt: “Explain the concept of blockchain technology in simple terms, including its key benefits and applications.”

This prompt is clear, specific, and provides context for my response.

Example 2: Providing information on a topic

Prompt: “Provide an overview of the history of quantum computing, including its development, major milestones, and current state. Include references to relevant studies or publications.”

This prompt specifies the type of information you’re looking for (overview) and asks me to provide specific details (history, milestones, current state).

Example 3: Generating creative content

Prompt: “Write a short story about a character who discovers a hidden world within their reflection. Include themes of identity, self-discovery, and personal growth.”

This prompt is more open-ended but still provides enough context for me to understand the task (writing a short story) and the desired tone (creative, imaginative).

By following these guidelines, you can craft effective prompts that elicit accurate, informative, and engaging responses from language models like myself.