This is about how a determined high-agency would go about the process of rapidly leveling up skills and building a moderately compelling “vibe engineering” competency … the use of that competency might be as modest as being able to follow news in the area and make appropriate stock investments … but it is possibly even something that would lead to start-up opportunities or consultancies.

Better Understanding Vibe Engineering: An Engineering Approach That Is Currently Being Developed

First, let’s clarify “vibe engineering” and what kinds of toolchains, frameworks, toolkits and computing resources are required.

  • Understanding the Nuances of LLM Outputs: Beyond simple functionality, it’s about crafting systems that excel at eliciting engineering requirements, through interactions that furnish helpful, informative, qualitative responses.

  • Contextual Awareness: LLMs are highly sensitive to context. “Vibe engineering” means mastering the art of natural language chat for interactions that successively shape that context for specific outcomes.

  • Iterative Refinement: Vibe engineering is being more aggressive at iteratively thinking, re-thinking, re-re-thinking; completely outside-the-box except with assistance from the accumulated knowledge embedded in LLM models.

Vibe engineering is an experimental process, primarily in the virtual realm, involving prompt engineering, fine-tuning, and system design to achieve the desired “vibe” but it’s important to note that the responses from virtual realm process can be extended to include physical, even non-compute / non-AI experimentation in the lab with microscopy / instruments, on the test bench with electrical instruments, in the robotics shop or doing in situ experiments in the field or garden.

6-Month Level-Up Plan

Phase 1: Foundations (Month 1-2)

  • Master the LLM Landscape:

    • Hands-on Exploration: Get API keys for Gemini, Claude, ChatGPT, etc. Experiment with basic prompts, analyze outputs, and compare model behaviors. Make lots of mistakes, learn how to fail, how to ask questions, how to overcome obstacles, how to see around corners.

    • Open Source LLM Deep Dive: Get familiar, several times each week, with new developments on HuggingFace Models and Ollama, be conversant with best ways to run smallish LLMs LOCALLY in order to better understand issues in operating with larger LLMs using various cloud GPU compte alternatives. At minimum, investigate, install and run Gemma, QwQ, DeepSeek, Mistral, Llama, Phi-4, maybe Nomic and other open-source models from tencent, perplexity, allenAI, et al locally (even on a modest GPU). Understand the models architecture and limitations, as well as the practical nuts-and-bolts stuff about running the models in containers.

    • Study Prompt Engineering: Don’t just learn techniques like few-shot prompting, chain-of-thought, and prompt chaining but start understanding something about the large landscape of what is happening in prompt engineering and the new, future directions in which developments in this area like reasoning with large language models (RLLMs) are evolving.

    • Follow Research; Learn To Conduct Business Intelligence: Curate your own lists of X accounts, fora, blogs and pre-print sources and RSS feeds to stay updated on the latest LLM advancements through Arxiv AI papers, blogs that you trust, and new industry publications that people follow. This is primarily about learning the process of continual content curration – it’s not about having the definitive list … the landscape will change.

  • GPU Compute Basics:

    • Cloud GPU Platforms: Create free-tier accounts on AWS, GCP, Azure, Oracle, IBM and different traditional cloud providers in the GPU marketplace and develop a workable stragety to explore different GPU offerings without incurring much in the way of cost.

    • Decentralized GPU: Experiment with Akash Network, Vast.ai, Paperspace, and RunPod. Understand their pricing models and deployment processes. Carefully develop a workable stragety to explore different GPU offerings without incurring much in the way of cost.

    • Containerization: Master Docker and Kubernetes beyond basic usage. While AI/LLM focus isn’t container internals, proficiency matters. Explore minimal containers (scratch images, Alpine, Wolfi, BottleRocket, distroless) for reduced attack surfaces and faster deployments. Deepen container knowledge for reliable LLM execution (local/cloud). Pursue mastery in k8s/micro k8s, multi-stage Docker alternatives, telemetry integration for observable LLM applications across GPUs, and database testing (SQLite, PostgreSQL, Neo4j) with persistent data stores like Confluence.

  • Tooling:

    • Monitoring Tools: It is IMPERATIVE to FIRST learn to really USE monitoring, eg Prometheus including Node Exporter, Grafana, AWS Cloudwatch, Azure Monitor, GCP Monitoring, NVIDIA Data Center GPU Management (DCGM) exporter and any platform’s billing dashboards for tracking LLM performance, cost, and latency … you don’t want to run up a gigantic bill in order to pay tuition for this kind of education, so learn monitoring FIRST. Then MONITOR and learn what you don’t know about monitoring, learn more and MONITOR and start developing an awareness of what you might be missing and then learn how to MONITOR, trace, log that also!

    • LangChain/LlamaIndex: Develop proficiency in LangChain, LlamaIndex, and related models/frameworks through hands-on experimentation on local hardware. While the ultimate objective is to build LLM-powered applications leveraging cloud compute and rentable GPUs, initial learning can be effectively achieved without a high-end AI workstation. A capable local AI workstation ($3,000 - $5,000) is recommended, acknowledging that it will not match cloud compute performance. As demonstrated by the the DeepSeek lesson or Manus lesson, constrained hardware environments often facilitate deeper understanding and more effective learning than readily available abundant resources.

    • Hugging Face Transformers: Master this evolving library for current and future LLM applications. Due to rapid advancements, prioritize developing agile tech intelligence gathering skills to maintain proficiency amidst accelerating change.

Phase 2: Building Expertise (Month 3-4)

  • Focus on Interoperability:

    • Develop Abstraction Layers: Create code libraries that allow seamless switching between different LLM APIs and GPU providers.

    • Standardize Data Formats: Define data formats for input and output that can be used across different LLMs.

    • Experiment with Orchestration: Build systems that can dynamically select the best LLM and GPU resources for a given task.

  • “Vibe Engineering” Experiments:

    • Create Case Studies: Develop small projects that demonstrate the ability to control the “vibe” of LLM outputs (e.g., a chatbot that adapts its tone based on user input).

    • Analyze Emotional Responses: Use sentiment analysis and other tools to measure the emotional impact of LLM outputs.

    • Document Your Findings: Create a portfolio of “vibe engineering” techniques and best practices.

  • Cost Optimization:

    • Benchmark GPU Providers: Compare the performance and cost of different GPU providers for various LLM workloads.

    • Implement Cost-Saving Strategies: Use techniques like spot instances, model quantization, and efficient prompt design to minimize costs.

    • Develop Cost Estimation Tools: Create tools that can predict the cost of running LLM applications on different platforms.

Phase 3: Evangelism and Community Building (Month 5-6)

  • Open Source Contributions:

    • Develop Toolkits: Create open-source libraries and tools that simplify LLM interoperability and “vibe engineering.”

    • Contribute to Existing Projects: Contribute to LangChain, LlamaIndex, and other relevant open-source projects.

    • Share Your Knowledge: Write blog posts, create tutorials, and give presentations on your work.

  • Community Engagement:

    • Join Online Forums: Participate in discussions on Reddit, Discord, and other online forums.

    • Attend Meetups and Conferences: Network with other LLM developers and researchers.

    • Build a Social Media Presence: Share your work and insights on Twitter, LinkedIn, and other platforms.

  • Consultancy Development:

    • Define Your Services: Clearly define the services you offer (e.g., LLM strategy, toolchain development, “vibe engineering” workshops).

    • Create a Website and Portfolio: Showcase your expertise and experience.

    • Develop Pricing Models: Determine your hourly rates and package your services.

Key Success Factor Areas

Aggressively ask AI for pertinent suggestions in each of these factor areas throughout the process of leveling up skills, ie always try to be more cognizant of what you have missed.

  • Hands-on Learning: Focus on practical experimentation and building real-world projects.

  • Community Engagement: Actively participate in the open-source community.

  • Continuous Learning: Stay up-to-date on the rapidly evolving LLM landscape.

  • Clear Communication: Effectively communicate your expertise and value proposition.

  • Ethical Considerations: Always keep in mind the ethical implications of LLM technology.

By focusing on coaching yourself in these factor areas, you can build a stronger foundation for your “vibe engineering” consultancy and find more concrete ways to contribute to the responsible development of LLM technology.

1. Hands-on Learning: Focus on Practical Experimentation and Building Real-World Projects

  • Why It’s Crucial: Theoretical knowledge is insufficient in the rapidly evolving LLM field. Practical experience is the cornerstone of expertise. Building projects allows you to encounter real-world challenges, understand the nuances of different models, and develop problem-solving skills.
  • Specific Strategies:
    • Start Small, Iterate Often: Begin with simple projects (e.g., a chatbot that generates different tones) and gradually increase complexity.
    • Reproduce Research Papers: Implement the techniques described in research papers to gain a deeper understanding of cutting-edge methods.
    • Build a Portfolio: Create a GitHub repository or website showcasing your projects and code.
    • Experiment with Different Use Cases: Explore diverse applications of LLMs, such as content generation, code generation, data analysis, and creative writing.
    • Focus on Debugging and Troubleshooting: Learn to identify and resolve common LLM errors, such as hallucinations, biases, and performance issues.
    • Resource Suggestions:
      • Kaggle: For datasets and code notebooks.
      • Hugging Face Spaces: For deploying and sharing your projects.
      • Google Colab/Jupyter Notebooks: For interactive coding and experimentation.
      • Local LLM Libraries: Investigate projects that allow running LLMs locally, even on modest hardware.

2. Community Engagement: Actively Participate in the Open-Source Community

  • Why It’s Crucial: The LLM field thrives on collaboration and knowledge sharing. Engaging with the open-source community provides access to valuable resources, feedback, and networking opportunities.
  • Specific Strategies:
    • Contribute to Existing Projects: Identify projects that align with your interests and contribute code, documentation, or bug reports.
    • Create Your Own Open-Source Tools: Develop libraries or frameworks that address specific needs in the LLM community.
    • Participate in Online Forums: Engage in discussions on Reddit, Discord, and other online platforms.
    • Attend Meetups and Conferences: Network with other LLM developers and researchers.
    • Share Your Knowledge: Write blog posts, create tutorials, and give presentations on your work.
    • Questions to Ponder:
      • What are the key pain points in the LLM community that I can help address?
      • What unique skills or perspectives can I bring to the community?
      • How can I build meaningful relationships with other community members?

3. Continuous Learning: Stay Up-to-Date on the Rapidly Evolving LLM Landscape

  • Why It’s Crucial: The LLM field is constantly evolving, with new models, techniques, and applications emerging rapidly. Continuous learning is essential for staying competitive and providing cutting-edge solutions.
  • Specific Strategies:
    • Follow Research Papers: Regularly read and analyze research papers from leading institutions.
    • Subscribe to Industry Newsletters and Blogs: Stay informed about the latest developments in the LLM field.
    • Take Online Courses and Tutorials: Enhance your skills and knowledge through structured learning.
    • Experiment with New Models and Techniques: Stay ahead of the curve by exploring emerging technologies.
    • Build a Personal Learning Network: Connect with experts and other learners in the LLM field.
    • Resource Suggestions:
      • Arxiv: For pre-print research papers.
      • Google AI Blog, OpenAI Blog, DeepMind Blog: For industry updates.
      • Coursera, edX, Udemy: For online courses.

4. Clear Communication: Effectively Communicate Your Expertise and Value Proposition

  • Why It’s Crucial: As a consultant, you need to be able to clearly communicate your expertise and value proposition to potential clients. Effective communication is essential for building trust and establishing credibility.
  • Specific Strategies:
    • Develop a Strong Online Presence: Create a professional website and LinkedIn profile showcasing your skills and experience.
    • Create Compelling Content: Write blog posts, articles, and case studies that demonstrate your expertise.
    • Practice Public Speaking: Develop your presentation skills to effectively communicate your ideas.
    • Tailor Your Communication to Your Audience: Adapt your language and messaging to the specific needs and interests of your clients.
    • Focus on Problem-Solving: Emphasize how your services can help clients solve their specific challenges.
    • Questions to Ponder:
      • What are the key benefits that I offer to my clients?
      • How can I effectively communicate my expertise to a non-technical audience?
      • What are the most effective channels for reaching my target clients?

5. Ethical Considerations: Always Keep in Mind the Ethical Implications of LLM Technology

  • Why It’s Crucial: LLMs have the potential to be used for both good and bad purposes. As a consultant, you have a responsibility to consider the ethical implications of your work and promote responsible AI development.
  • Specific Strategies:
    • Stay Informed About Ethical Issues: Educate yourself about the potential biases, risks, and unintended consequences of LLMs.
    • Promote Responsible AI Practices: Advocate for ethical guidelines and best practices in the LLM field.
    • Consider the Impact of Your Work: Evaluate the potential social and environmental impact of your projects.
    • Be Transparent and Accountable: Clearly communicate the limitations and potential risks of LLM technology.
    • Engage in Ethical Discussions: Participate in conversations about the ethical implications of AI.
    • Questions to Ponder:
      • How can I ensure that my work is aligned with ethical principles?
      • What are the potential biases and risks associated with the LLMs I use?
      • How can I mitigate the negative impacts of LLM technology?