From Novice to Recursive Architect
The journey begins with the user finding out about ChatGPT.
β‘οΈ Leads to: B (Initial Usage Mode)
User decides how they'll initially engage with the tool.
User starts by asking simple questions out of curiosity.
β‘οΈ Leads to: G (Learn Prompt-Response Basics)
User tries to get assistance with a specific, real-world task.
β‘οΈ Leads to: G (Learn Prompt-Response Basics)
User finds online communities focused on ChatGPT and prompting.
β‘οΈ Leads to: F (Observe Sample Interactions)
User learns by observing how others interact with ChatGPT.
β‘οΈ Leads to: C (Prompt w/ Basic Questions)
User begins to understand the fundamental mechanics of how prompts elicit responses.
β‘οΈ Leads to: H (Experiment: Rephrase, Retry, Tweak)
User actively experiments by changing prompts to see different outcomes. This is an iterative loop.
π Experimentation Loop: Returns to H if not satisfied.
β‘οΈ Leads to: I (Satisfied?)
User evaluates if the current results meet their needs.
User seeks out established prompt structures and examples to improve their results.
β‘οΈ Leads to: K (Read Help, Docs, or Tutorials)
User consults official documentation, guides, or tutorials to deepen understanding.
β‘οΈ Leads to: L (Notice Model Limits?)
User starts to perceive the boundaries and limitations of the model.
User develops a clearer understanding of what the model can and cannot do reliably.
β‘οΈ Leads to: N (Adopt Structured Prompt Syntax)
User begins to use more formal and structured ways of writing prompts.
β‘οΈ Leads to: O (Learn System/Instruction Prompts)
User learns about providing context, roles, and constraints via system messages or explicit instructions.
β‘οΈ Leads to: P (Use "Act as", rules, goals)
User employs techniques like role-playing ("Act as a..."), defining rules, setting goals, and giving meta-instructions.
β‘οΈ Leads to: Q (Organize Prompt Libraries)
User starts curating and organizing their effective prompts for reuse.
β‘οΈ Leads to: R (Chain Multi-Step Tasks)
Potential Risks: Context Transfer Failure (CTX1), Latent Bleed (LB1), No Explicit Metrics (ME1).
User breaks down complex tasks into sequential prompts, using output from one step as input for the next.
β‘οΈ Leads to: S (Realize Prompt Engineering is a Skill)
User recognizes that crafting effective prompts is a distinct and valuable skill.
β‘οΈ Leads to: T (Study Community-Shared Prompts)
User actively learns from advanced prompts and meta-prompting techniques shared by the community.
β‘οΈ Leads to: U (Use External Tools?)
User considers integrating external tools, files, or advanced functionalities like Code Interpreter.
Without external tools or further advancement, user may feel they've reached the limits of basic prompting.
User starts using plugins, uploading files, or leveraging Code Interpreter for more complex tasks.
β‘οΈ Leads to: X (Automate Data/Workflow Integration)
Potential Risk: Token Usage Spike/Latency (TE1).
User looks for ways to automate the integration of data and workflows with ChatGPT.
β‘οΈ Leads to: Y (Debug Output, Validate Model Reasoning) (Advanced Stage)
User critically examines outputs, attempts to understand the model's reasoning, and debugs unexpected results.
β‘οΈ Leads to: Z (Analyze Output Bias/Error)
Related to: HM4 (Sanitize/Filter Output).
User becomes aware of and analyzes potential biases or systematic errors in model outputs.
β‘οΈ Leads to: AA (Perform Side-by-Side Model Testing)
Potential Risk: Hallucination Suspected (HM1).
User compares outputs from different models or different prompting strategies for the same task.
β‘οΈ Leads to: AB (Want More Control?)
User desires deeper control over the model's behavior and integration capabilities.
User starts exploring programmatic access via APIs, SDKs, or other automation tools.
β‘οΈ Leads to: AD (Build ChatGPT-Integrated Apps/Scripts)
User begins developing custom applications or scripts that leverage ChatGPT's capabilities.
β‘οΈ Leads to: AE (Connect LLMs w/ External APIs/Data)
User integrates LLMs with other APIs and external data sources to create more powerful solutions.
β‘οΈ Leads to: AF (Test Prompt Security)
User investigates prompt security, including jailbreaking attempts and adversarial prompting techniques.
β‘οΈ Leads to: AG (Study Prompt Injection Prevention)
Potential Risks: Persistent State Threat (DS1), Prompt-Stuffed Payload Injection (DS7).
User learns about methods to prevent prompt injection and other security vulnerabilities.
β‘οΈ Leads to: AH (Develop Domain-Specific Frameworks)
User creates tailored prompting frameworks or methodologies for specific domains or tasks.
β‘οΈ Leads to: AI (Chain Prompts Into Workflows/Agents)
User designs complex workflows or simple agents by chaining multiple prompts and logic.
β‘οΈ Leads to: AJ (Implement Feedback, Memory, Re-prompt Loops)
Potential Risks: Prompt Complexity Ceiling (CL1), Async Agent Split/Delegation (CL4).
Can also be a point for CL3 (Delegate/Automate?) decision if complexity becomes too high.
User builds systems with feedback mechanisms, short-term memory, and automated re-prompting logic.
β‘οΈ Leads to: AK (Monetize/Deploy?)
Potential Risks: Agent Goal Drift (AL1), Prompt Version Drift (VR1).
User considers commercializing their prompt-based solutions or deploying them at scale.
User prepares their tools/prompts for distribution, licensing, or sale.
β‘οΈ Leads to: AM (Setup Payment)
User sets up payment processing systems for their monetized offerings.
β‘οΈ Leads to: AN (Implement License Validation + DRM)
User implements mechanisms for license validation or Digital Rights Management.
β‘οΈ Leads to: AO (Publish on Marketplace/Portal)
User makes their tools or prompts available on marketplaces or dedicated portals.
β‘οΈ Leads to: AP (Attack/Stress-Test Own Prompts?)
User considers proactively testing their own prompts for vulnerabilities and robustness.
User performs red teaming exercises, including adversarial attacks, fuzzing, and simulating abuse cases.
β‘οΈ Leads to: AR (Patch, Harden, Iterate)
User applies patches, hardens their prompts/systems, and iterates based on testing results.
β‘οΈ Leads to: AS (Repeat Deployment/Testing)
Feedback from: BC (Systematize Prompt Audits) via Red Team Feedback Injection.
User establishes a cycle of deploying updates and continuously testing their prompt-based systems.
β‘οΈ Leads to: AT (Use GPT for Prompt/Agent Generation) (Meta Mastery Stage)
User leverages GPT itself to generate, refine, or optimize prompts and agentic structures.
β‘οΈ Leads to: AU (Recursive Prompt Evolution/Optimization)
User develops systems where prompts can evolve or be optimized recursively, potentially by AI.
β‘οΈ Leads to: AV (Build Modular, Reusable Prompt Libraries)
π Recursive Mastery Loop: Can connect from BD (Optimize Workflows) and BG (Recursive Mastery Loop).
User designs and curates highly modular and reusable libraries of prompts or prompt components.
β‘οΈ Leads to: AW (Design Plug-n-Play Chains/Composables)
User creates prompt chains or composable units that can be easily combined and reconfigured.
β‘οΈ Leads to: AX (Simulate Multi-Agent/Role-Play Interactions)
User designs sophisticated simulations involving multiple AI agents or complex role-playing scenarios.
β‘οΈ Leads to: AY (Interop w/ Multiple LLMs)
User works with multiple LLMs, comparing their strengths and potentially blending their outputs for superior results.
β‘οΈ Leads to: AZ (Incorporate Autonomous Systems?)
User considers building or integrating fully autonomous AI systems or Decentralized Autonomous Organizations (DAOs).
User actively develops autonomous agents or DAOs powered by GPT or similar LLMs.
β‘οΈ Leads to: BB (Implement Self-Improving Prompts/Reflexive Loops)
User designs systems where prompts can self-improve or adapt through reflexive feedback loops.
β‘οΈ Leads to: BC (Systematize Prompt Audits, Logging, Analytics)
User establishes systematic processes for auditing prompts, logging interactions, and analyzing performance data.
β‘οΈ Leads to: BD (Optimize, Abstract, Document Workflows)
𧨠Red Team Feedback Injection: Feeds into AR (Patch, Harden, Iterate).
User focuses on optimizing, abstracting, and thoroughly documenting their advanced prompting workflows.
β‘οΈ Leads to: BE (Attain .01% GPT Mastery β Recursive Architect)
π Recursive Mastery Loop: Can loop back to AU (Recursive Prompt Evolution).
User reaches a state of profound mastery, capable of architecting recursive and highly sophisticated AI systems.
β‘οΈ Leads to: BF (Evolve to Self-Propagating, Monetizing AI Products) and BH (Study Regulatory Vectors) (Omniaware Stage)
User's creations potentially evolve into self-propagating or autonomously monetizing AI products.
β‘οΈ Leads to: BG (Recursive Mastery Loop)
Path to Mentorship: Can lead to BN (Mentor Community).
A continuous loop of mastery, feeding back into recursive prompt evolution and optimization.
π Returns to: AU (Recursive Prompt Evolution/Optimization)
User delves into the complex regulatory, copyright, and legal aspects surrounding AI and LLMs.
β‘οΈ Leads to: BI (Track Artifact Lineage)
User implements methods for tracking the lineage of AI-generated artifacts, possibly using signatures or watermarking.
β‘οΈ Leads to: BJ (Red Team / Blue Team Full Cycle)
User engages in comprehensive red team (offensive) and blue team (defensive) security exercises.
β‘οΈ Leads to: BK (Attack/Defend All System Layers)
User develops strategies to attack and defend all layers of their AI systems, from prompts to infrastructure.
β‘οΈ Leads to: BL (Zero-Trust, Immutable Pipeline Protocols)
User implements advanced security protocols like zero-trust architectures and immutable deployment pipelines.
β‘οΈ Leads to: BM (Syndicate Across Platforms)
User's expertise or tools become influential and are syndicated across various platforms, APIs, or toolsets.
β‘οΈ Leads to: BN (Mentor Community)
User gives back by mentoring others, publishing authoritative guides, or sharing advanced meta-prompts.
β‘οΈ Leads to: BO (Lead OpenAI/Core LLM Ecosystem Evolution)
Can be reached from: BF (Evolve to AI Products), DS11 (Rolling Forensics), AL5 (Validate, Realign, Rebase), VR4 (Rollback/Hotfix).
User becomes a leading figure, contributing significantly to the evolution of the core LLM ecosystem.
A point of stagnation or failure. The user might churn, revert to earlier stages of usage, or find a new path to evolve.
π Returns to: B (Initial Usage Mode) or requires a new approach.
This is a common outcome for many failure paths noted in the "Failure Modes" section.
This section highlights common points where users might get stuck or take alternative routes not explicitly part of the main progression. Many failure modes can lead to BP (Stagnation).
This section details potential issues, risks, and how they might be addressed or lead to problems if unmanaged. Many unaddressed issues can lead to BP (Stagnate/Churn).