• Description: This foundational level involves employees using readily available LLM tools (like ChatGPT, Claude, Gemini, Microsoft Copilot, Perplexity or internal AI tools) to augment their daily tasks – including research, drafting emails and reports, brainstorming ideas, summarizing documents, learning new concepts, and performing data analysis.

• Maturity & Adoption Landscape: While experimentation is widespread, effective adoption creating true “AI power users” is rare. Most usage is inefficient, often employing the wrong models, tools, or prompting techniques for specific tasks, leading to subpar results. Critically, usage is frequently insecure, bypassing necessary company controls and potentially exposing sensitive data.

• The Human Element: Becoming an AI power user isn’t just about knowing niche prompt engineering techniques; it’s about becoming a skilled collaborator. This requires actively bringing your unique human strengths:
  • Domain Expertise: Infusing prompts with your industry knowledge, specific context, and nuanced understanding.
  • Clear Instruction: Crafting precise instructions, providing clarifying examples, setting necessary constraints, and defining the desired output format or persona.
  • Resourcefulness: Identifying high-quality, relevant information or data sources to provide to the AI, enhancing its accuracy and relevance.
  • Critical Judgment: Rigorously evaluating AI outputs for factual accuracy, logical consistency, potential bias, and overall relevance; knowing when the AI’s output is unreliable or requires significant correction.
  • Iterative Feedback: Skillfully guiding the AI through cycles of refinement, providing constructive feedback to steer it towards better results. AI is a powerful tool, but it lacks human intuition, common sense, and deep contextual understanding – your active input is essential to bridge this gap.

• Timeframe & Effort for Results: Basic interaction is instant. However, progressing from casual use to achieving reliable, significant productivity gains requires developing these collaborative skills through structured learning and consistent practice over weeks and months.

• Key Techniques & Tools:
  • Core LLM Tools (Secure Usage Recommended): ChatGPT (Plus/Team/Enterprise), Claude (Pro/Team), Google Gemini Advanced, Microsoft Copilot (Enterprise-integrated versions), Perplexity (for AI-powered search and research), Custom Internal AI Tools.
  • Essential Skills: Effective Prompt Engineering (Zero-shot, Few-shot, Chain-of-Thought, specifying persona/format/tone), Context Provisioning (providing relevant text/data within prompts), Iterative Refinement techniques, Critical Output Evaluation & Fact-Checking, Understanding Model Strengths/Weaknesses (e.g., creativity vs. factual recall), Secure Usage Practices (using enterprise accounts, data anonymization awareness), Strategic Tool Selection (matching the right AI model and tool to the specific task).

• Common Pitfalls & Challenges:
  • • Security & Privacy Risks: Using free consumer tools with confidential company, customer, or personal data.
    • Lack of Imagination: It is hard to know where to get started with bringing AI into daily tasks and where it can add value.
    • Inaccuracy & Hallucinations: Blindly trusting AI outputs without verification, leading to factual errors, nonsensical statements, and poor decisions.
    • Inefficiency & Wasted Time: Poor prompting yields generic, unhelpful responses; using the wrong tool for the task.
    • Lack of Strategic Application: Using AI sporadically or for low-value tasks instead of integrating it thoughtfully into high-impact workflows.
    • Output Inconsistency: Getting different or conflicting answers from the same or different models over time.
    • Failing to integrate human judgment effectively.
      

• How Towards AI Helps: We focus heavily on teaching this collaborative approach: how to use AI tools safely, effectively, and strategically in real workflows. Through our (Business Professionals Track) and Custom Live Bootcamps, we train teams to match the right models to the right tasks, apply proven prompting techniques, integrate domain expertise, critically evaluate outputs, and build high-impact daily AI workflows. The result: faster decisions, sharper execution, and real productivity gains.

• Description: This foundational level involves employees using readily available LLM tools (like ChatGPT, Claude, Gemini, Microsoft Copilot, Perplexity or internal AI tools) to augment their daily tasks – including research, drafting emails and reports, brainstorming ideas, summarizing documents, learning new concepts, and performing data analysis.
• Maturity & Adoption Landscape: While experimentation is widespread, effective adoption creating true “AI power users” is rare. Most usage is inefficient, often employing the wrong models, tools, or prompting techniques for specific tasks, leading to subpar results. Critically, usage is frequently insecure, bypassing necessary company controls and potentially exposing sensitive data.
• The Human Element: Becoming an AI power user isn’t just about knowing niche prompt engineering techniques; it’s about becoming a skilled collaborator. This requires actively bringing your unique human strengths:
  • Domain Expertise: Infusing prompts with your industry knowledge, specific context, and nuanced understanding.
  • Clear Instruction: Crafting precise instructions, providing clarifying examples, setting necessary constraints, and defining the desired output format or persona.
  • Resourcefulness: Identifying high-quality, relevant information or data sources to provide to the AI, enhancing its accuracy and relevance.
  • Critical Judgment: Rigorously evaluating AI outputs for factual accuracy, logical consistency, potential bias, and overall relevance; knowing when the AI’s output is unreliable or requires significant correction.
  • Iterative Feedback: Skillfully guiding the AI through cycles of refinement, providing constructive feedback to steer it towards better results. AI is a powerful tool, but it lacks human intuition, common sense, and deep contextual understanding – your active input is essential to bridge this gap.
• Timeframe & Effort for Results: Basic interaction is instant. However, progressing from casual use to achieving reliable, significant productivity gains requires developing these collaborative skills through structured learning and consistent practice over weeks and months.
• Key Techniques & Tools:
  • Core LLM Tools (Secure Usage Recommended): ChatGPT (Plus/Team/Enterprise), Claude (Pro/Team), Google Gemini Advanced, Microsoft Copilot (Enterprise-integrated versions), Perplexity (for AI-powered search and research), Custom Internal AI Tools.
  • Essential Skills: Effective Prompt Engineering (Zero-shot, Few-shot, Chain-of-Thought, specifying persona/format/tone), Context Provisioning (providing relevant text/data within prompts), Iterative Refinement techniques, Critical Output Evaluation & Fact-Checking, Understanding Model Strengths/Weaknesses (e.g., creativity vs. factual recall), Secure Usage Practices (using enterprise accounts, data anonymization awareness), Strategic Tool Selection (matching the right AI model and tool to the specific task).
• Common Pitfalls & Challenges:
  • Security & Privacy Risks: Using free consumer tools with confidential company, customer, or personal data.
  • Lack of Imagination: It is hard to know where to get started with bringing AI into daily tasks and where it can add value.
  • Inaccuracy & Hallucinations: Blindly trusting AI outputs without verification, leading to factual errors, nonsensical statements, and poor decisions.
  • Inefficiency & Wasted Time: Poor prompting yields generic, unhelpful responses; using the wrong tool for the task.
  • Lack of Strategic Application: Using AI sporadically or for low-value tasks instead of integrating it thoughtfully into high-impact workflows.
  • Output Inconsistency: Getting different or conflicting answers from the same or different models over time.
  • Failing to integrate human judgment effectively.
• How Towards AI Helps: Our AI Acceleration Program (Business Professionals Track) and Custom Live Bootcamps focus heavily on teaching this collaborative approach – how to leverage human expertise alongside AI tools safely, effectively, and strategically.

• Description: Utilizing AI-powered tools (like GitHub Copilot, Cursor IDE, ChatGPT, Gemini) specifically designed to assist developers throughout the software development lifecycle – including code generation, completion, debugging, testing, documentation, and refactoring.

• Maturity & Adoption Landscape: This is one of the more mature areas for AI application, with tools like GitHub Copilot seeing significant uptake for basic code completion. However, deep integration across the entire SDLC varies greatly. Top developers are increasingly using AI as a true pair programmer, but many others only scratch the surface. The potential for productivity gains is immense but often unrealized.

• The Human Element: Maximum productivity gains come from partnership, not blind delegation. Developers are essential collaborators, bringing their:
  • Architectural Understanding: Guiding AI code generation to fit within the broader system design, existing patterns, and constraints.
  • Coding Expertise & Standards: Writing effective prompts that specify languages, frameworks, desired logic, edge cases, and adhering to team coding standards. Critically reviewing AI suggestions for correctness, efficiency, maintainability, and security vulnerabilities.
  • Debugging Skills: Applying systematic human problem-solving to troubleshoot complex issues, potentially using AI to generate hypotheses or explain code sections.
  • Testing Intuition & Strategy: Using AI to rapidly generate test cases but applying human judgment to ensure comprehensive coverage, meaningful assertions, and testing of critical edge cases.

• Timeframe & Effort for Results: Basic code completion offers immediate, though sometimes minor, time savings. Achieving substantial productivity improvements requires developers to consciously adapt their workflows, master prompting for code, learn to critically evaluate suggestions quickly, and potentially adopt AI-native tools, typically taking consistent effort over several months.

• Key Techniques & Tools:
  • Code Assistance Tools: GitHub Copilot
  • Chatbots for Code: Using models like GPT-4o, Claude 3.5, Gemini via prompts for specific code generation, explanation, refactoring, or bug fixing tasks.
  • AI-Native IDEs: Cursor IDE (integrating AI deeply into the development environment).
  • Essential Skills: Prompting for Code Generation (specifying requirements clearly), Critically Reviewing AI Code Suggestions (identifying errors, inefficiencies, security flaws), AI-Assisted Debugging techniques, Using AI for Unit Test Generation & Boilerplate Code, Leveraging AI for Code Documentation & Explanation, Integrating AI feedback into code reviews, Potential use in CI/CD for automated checks or summaries, Awareness of AI-specific Security Scanning tools.

• Common Pitfalls & Challenges:
  • Code Quality & Security Issues: AI generating functional but buggy, inefficient, non-idiomatic, or insecure code.
  • Over-reliance & Skill Degradation: Developers becoming less proficient in fundamental algorithms or language details.
  • Integration Friction: Difficulty merging AI-generated code smoothly with existing, complex codebases.
  • Debugging Opaque Code: Understanding and debugging intricate AI-generated logic can sometimes be challenging.
  • Licensing & Intellectual Property Concerns: Uncertainty regarding the origins and permissible use of AI-generated code snippets.
  • Lack of Project Context: AI tools often lacking the full context of the project, leading to locally correct but globally suboptimal suggestions.
  • Treating AI as an infallible oracle rather than a powerful, but fallible, assistant.

• How Towards AI Helps: We train developers to use AI without breaking your codebase or security model. Our Advanced LLM Developer Program and Bootcamps cover everything: how to prompt for high-quality code, integrate AI tools into real workflows, review outputs for bugs and vulnerabilities, and stay compliant with IP policies. This isn’t about code completion tricks—it’s about speeding up delivery while protecting quality. We make sure AI actually fits your engineering standards and delivers real ROI.

Description:
Moving beyond off-the-shelf solutions to design, build, and deploy custom applications powered by LLMs. This includes:

• Internal Tools & Agents: Tailored precisely to specific internal business processes, proprietary data, and unique workflow needs (e.g., internal knowledge base Q&A bots, automated summarization of internal reports, agents analyzing internal data patterns, intelligent routing for support tickets, extracting info from internal documents). The goal is typically significant internal efficiency, improved work quality, or leveraging unique knowledge securely.

• External-Facing Product Features: Integrating sophisticated, custom LLM-powered features directly into customer-facing products or services to create novel value propositions, enhance user experience significantly, or enable entirely new functionalities (e.g., AI writing assistants, hyper-personalized recommendations, advanced customer self-service interfaces, creative content generation tools). The goal shifts towards market differentiation and direct user value.

Maturity & Adoption Landscape:
Building custom internal tools shows rapidly growing interest and significant potential for operational gains, but actual deployment maturity is often lower than basic AI usage, with many companies in exploratory or pilot stages. This is where unique internal advantages can be built. Integrating features into external products represents the current frontier, led by tech leaders and startups; success here can be transformative, defining new product categories (e.g., Perplexity AI, Anysphere/Cursor).

The Human Element:
Building effective custom LLM solutions is a deeply collaborative process demanding human expertise throughout:

• Subject Matter Expertise: Crucial for both internal (end-users defining needs, validating outputs) and external (product managers defining value, understanding user needs) applications. Identifying and preparing the right data sources (internal documents, user data) is key.

• Developer & Engineering Expertise: Essential human skills in robust system design, data engineering pipelines (cleaning, transforming, embedding data), selecting appropriate models/frameworks, designing effective evaluation strategies, and reliable deployment are required for both. Experienced judgment is vital for choosing the right technical approach (e.g., advanced RAG vs. fine-tuning).

• Iterative Feedback Loops: Critical for internal tools (feedback from employee users) and even more complex for external products (requiring mechanisms for capturing feedback from a diverse, uncontrolled user base).

• Controlled vs. Uncontrolled Usage & Governance: A key distinction emerges here.

  • Internal: Advantage lies in implementing company policies, mandatory training, and clear usage guidelines, often enforcing human-in-the-loop verification for critical decisions, thus managing reliability within a known user base.

  • External: Faces diverse, unpredictable user interactions. You cannot easily enforce workflows or guarantee users understand limitations. This necessitates a significantly higher baseline of intrinsic reliability and safety built directly into the AI system. Development requires broader, deeper expertise spanning product management, UX design (for intuitive AI interaction), data science (for alignment), legal (compliance, IP), and ethics (bias, fairness, responsible AI).

Timeframe & Effort for Results:
While a basic internal demo might be built quickly, creating a robust, reliable, value-enhancing internal tool often requires ~300 hours or more of focused development (including requirements, data prep, evaluation, iteration). Building a polished, reliable, scalable, safe, and differentiated external-facing LLM feature demands substantially more effort, typically ~600 hours or more for initial results, often scaling into large, ongoing R&D initiatives due to the stringent requirements for intrinsic reliability, safety, user experience, and performance at scale.

Key Techniques & Tools:
Both rely on foundational tools and techniques:

• Foundation Models: APIs (OpenAI, Anthropic, Google) or Open-Source Models (Llama, Mistral, etc.).

• LLM Application Frameworks: LangChain, LlamaIndex, etc.

• Vector Databases: Pinecone, Weaviate, Chroma, Qdrant, Deep Lake, etc. for RAG.

• Core Development Techniques: Retrieval-Augmented Generation (RAG – including advanced strategies), Prompt Engineering (complex prompts, chaining), Fine-tuning (where appropriate), Agentic Frameworks (designing tool-using agents), Robust Evaluation Methodologies, Data Preparation & Cleaning, Backend/API integration.

However, building external products places greater emphasis on and often requires:

• Advanced System Architectures: More complex RAG, sophisticated/hierarchical agentic systems.

• Model Alignment & Safety: Robust safety guardrails, content moderation, bias reduction techniques (potentially including RLHF/DPO requiring human preference data).

• Scalability & Performance Optimization: Highly optimized inference, low-latency APIs, robust cloud infrastructure.

• User Experience (UX) Design for AI: Crafting intuitive interfaces, managing expectations, designing feedback mechanisms specifically for AI interaction.

• Rigorous Evaluation & Monitoring: Extensive A/B testing, real-world user feedback loops, continuous monitoring for performance, drift, safety, and satisfaction.

• Multi-modality: Increasingly integrating image, audio, or video capabilities.

• Ethical & Legal Frameworks: Deep integration of responsible AI, compliance (GDPR, CCPA), IP management.

Common Pitfalls & Challenges:
Both internal and external projects risk:

• Poorly Defined Use Case/ROI.

• Data Challenges (quality, availability, prep effort).

• Choosing the Wrong Technical Approach (over/under-engineering).

• Weak or Missing Evaluation leading to unreliable outputs.

• Scalability & Maintenance Hurdles.

• Security Risks (especially with sensitive data).

• Low User Adoption (if not intuitive, trustworthy, or useful).

• Unmanaged Costs (API or infrastructure).

• Insufficient SME/End-User Involvement.

External products face these challenges with heightened sensitivity AND add significant additional risks:

• Reliability & Consistency at Scale: Ensuring predictable performance for large, diverse user bases.

• Safety, Misuse & Adversarial Attacks: Preventing harmful outputs and building resilience against bad actors is critical due to public exposure.

• Poor or Confusing User Experience: Frustrating AI interactions directly impact product success.

• High Operational Costs at Scale: Inference costs can become substantial.

• Achieving True Differentiation: Building unique value beyond easily replicated features.

• Latency Issues: Slow responses kill user engagement.

• Navigating Regulatory Uncertainty: Compliance with evolving AI laws globally.

• Significant Brand & Reputational Risk: Public failures or ethical issues cause severe damage because user interaction is uncontrolled and visible.

• Long-term Maintainability & Adaptability: Keeping complex systems performing as models and expectations evolve.

How Towards AI Helps:
Our Expert Consulting & Development offerings (Strategic Advisory and Hands-On Sprints) are designed for both contexts:

• For Internal Tools, we leverage our practical experience building and deploying robust LLM pipelines collaboratively with client SMEs. We help define strategy, choose the right tech, build reliable solutions, and avoid common pitfalls, ensuring tools solve real business problems and integrate effectively.

• For External Products, we provide crucial strategic guidance on navigating the significantly heightened complexities, risks, and reliability requirements. We help assess feasibility, design robust and safe architectures, emphasize rigorous design, alignment, and safety measures essential for customer-facing features, and provide hands-on development support via Sprints. We don’t just build—we help you decide what’s worth building and how to do it responsibly given the higher stakes.

Our Advanced LLM Developer Training equips your internal teams with the skills needed to build and maintain effective custom solutions, whether internal or external.

Our mission is to make AI more accessible – both to individuals and to organizations.

We access Generative AI teaching and development talent from our huge network of AI technical writers and community:

• We hire the best talent from our writer network and community to write and instruct our AI books and courses. The best of these then also work on our external LLM consultancy and development projects. We have ~15 AI experts on the team with a core focus on enterprise Large Language Model tools.
• Over 2,000 AI practitioners have written for our publication. We publish ~40 AI articles and tutorials weekly.
• We have a huge audience of AI developers with 400,000 followers, 130,000 subscribers to our weekly AI newsletter for AI professionals and 60,000 members in our ”Learn AI Together” Discord Community.
• We have developed many custom LLM pipelines and corporate training sessions both for external clients and internal use cases.

Our team combines AI experience with business, product and strategy expertise.

• Our Co-founder and CEO Louie Peters’ past experience includes Vice President in Investment Research at J.P. Morgan before 6 years building and advising AI startups.
• Our Co-founder and CTO Louis-François Bouchard has prior experience as Head of AI and recently left his AI phd at MILA. He has 60,000 followers of his AI lessons on Youtube and is also an O’Reilly instructor.