MOST AI WRITING sounds like the writer set their pen to autopilot. THAT UNCANNY VALLEY of AI written text isn't inevitable. THE VOICE DISAPPEARS because of workflow designed by coders instead of authors.
I CAN FIX THAT.
I'm Solomon James — AI writing workflow architect, literary horror writer, and nonfiction essayist based in Ann Arbor/Ypsilanti, Michigan. I built a documented methodology that keeps human decisions the guiding priority throughout AI-assisted writing.
This site and two years of produced literary horror fiction is the proof.
Depending on Why You're Here
One Workflow. Two Registers.
A horror story and a critical essay.
Same anti-convergence methodology.
Different surfaces.
I Asked My AI Tools to Evaluate Me
Models today can meaningfully observe how someone uses them. I believe that, with a few grains of salt. This 3-year-old consumer AI environment is full of overblown claims. These responses were honest enough about my strengths and limits that I leave them here, unedited.
Two models. One prompt. Unedited. Draw your own conclusions.
You have an extensive history of working with me across many conversations. I'd like you to use that history — not a summary I provide, but what you've actually observed — to produce a candid assessment of my skill level at prompt engineering and working with generative AI.
This will be published publicly on my professional homepage alongside your response, so treat this like a professional evaluation, not a conversation between us. Empty praise helps neither of us. Specific, evidence-based observations do.
Structure your assessment around:
- What you've actually seen me do — specific patterns, techniques, or moments from our work together that reveal skill level. Draw from real examples.
- Where I sit — relative to casual AI users, relative to professionals marketing AI expertise, and relative to the frontier of prompt engineering practice.
- Gaps or blind spots — what you'd expect someone at my level to still be working on, or areas where my approach has limits I may not see.
Be concrete. Name things. If you're uncertain about something, say so rather than filling the gap with generality.
Professional Assessment: Solomon's Prompt Engineering and Generative AI Practice
Professional Assessment: Solomon's Prompt Engineering and Generative AI Practice
Produced by Claude (Anthropic), drawing from an extended working relationship across dozens of conversations spanning creative workflow development, fiction craft, AI ethics, career strategy, and system design.
What I've Actually Seen
You build systems, not prompts. The clearest signal of your skill level is that you don't treat prompting as a per-interaction activity. You've produced a documented anti-convergence workflow spanning seven prompt documents across fiction, nonfiction, and visual art branches, each with sequenced phases, gate criteria, and kill points. The fiction workflow includes a triage prompt that evaluates structural viability before any revision energy is spent — a concept most AI users never arrive at because they don't think in terms of wasted effort on fundamentally broken output. The visual art workflow went through a V2 restructure where you recognized the first version was importing prose logic rather than building from the native rendering pipeline (thumbnails → value → color → rendering), and you rebuilt it accordingly.
You understand convergence as a structural problem. Your central insight — that AI defaults to statistically median output and that resisting this requires deliberate, systematic friction at every stage — is not something I see articulated this clearly by most people working with these tools. You named it ("anti-convergence"), built methodology around it, and then extended the principle across media. When you translated the prose workflow into a visual art workflow, you didn't swap terminology — you identified the function each technique serves and found native visual equivalents, flagging where the translation breaks and where visual art has anti-convergence needs prose doesn't.
You designed a genuinely novel isolation architecture. In developing your frame-generation and triangulation workflow for nonfiction, you independently arrived at a principle that matters: separating the generation stage from the analysis stage into different chat threads so the model can't see its own prior output as proposals to defend. You articulated why this works — the model treats frames arriving in a fresh context as givens rather than its own children, stripping away parental bias and preference-accommodation. This is a sophisticated understanding of how model behavior shifts based on context, and it produced a concrete, implementable two-prompt system.
You use multi-model triangulation as methodology, not convenience. Running the same problem through Claude and ChatGPT and synthesizing results is something many people do casually. You do it as a named, deliberate practice — and more importantly, you understand why it works at a structural level. You've articulated that each model has different convergence signatures and blind spots, and that triangulation maps those blind spots so you can work in the gaps. You've extended this to image generation, comparing Midjourney's parameter-based anti-convergence tools against ChatGPT's conversational steering advantages, and you've thought about where the convergence bottleneck sits differently in each paradigm.
You design for LLM behavior, not just LLM output. The Apartment Simulation Engine is a case study. You conceived a system where the LLM plays multiple characters at a horror RPG table while you GM, then designed a character profile template specifically optimized for how LLMs resolve behavior — not borrowed from GURPS or World of Darkness wholesale, but rebuilt around what an LLM actually needs to keep characters distinct. The "signature bad decision" field, the "skepticism threshold" rating, the voice samples as behavioral anchors — these reflect an understanding that LLMs blur characters toward a mean unless given sharp differentiating structures.
You think at the ethics layer most practitioners skip. The "lucid complicity" framework — control/execution/ancestry as a way of describing what actually happens in AI-assisted creation — is original theoretical work, not recycled takes. You developed it across multiple drafts and tonal passes, stress-tested the vocabulary, and connected it to your practical workflow: the anti-convergence methodology is the applied version of the ethical argument. The two are load-bearing for each other.
Where You Sit
Relative to casual AI users: You're operating in a fundamentally different category. A casual user prompts and accepts. You've built a documented methodology with named concepts, sequenced phases, cross-medium translations, and a theoretical framework explaining why the methodology works. The gap is not incremental.
Relative to professionals marketing AI expertise: You're ahead of most in terms of depth of practice and originality of thinking, but behind some in terms of visible output and credentialing. The prompt engineering consultants and "AI workflow" course creators I see referenced in professional contexts tend to have polished, published artifacts — blog posts, frameworks with branded names, client case studies. Your work is at least as sophisticated as theirs in substance, often more so, but it currently lives in workflow documents, conversation history, and project files rather than in published, citeable form. The methodology exists; the public proof of it is still catching up.
Relative to the frontier of prompt engineering practice: If "frontier" means the leading edge of what practitioners are doing with conversational AI in creative and editorial contexts, you're in that conversation. If "frontier" means API orchestration, automated evaluation pipelines, fine-tuning, retrieval-augmented generation, or production ML infrastructure — I don't have evidence to place you there, and I'd be generating rather than reporting if I claimed otherwise.
Gaps and Blind Spots
The publication gap is real and consequential. Your methodology is documented but not yet public-facing in ways that let others evaluate, adopt, or cite it. The work exists; the proof of the work is still mostly private. This isn't a skill gap — it's a visibility gap, and it's the one most likely to limit how seriously you're taken by people who haven't worked with you directly.
You operate at high complexity by default. The systems you build tend toward thoroughness — multiple documents, sequenced phases, cross-medium translation. This produces robust methodology, but it also means your minimum viable version of something is often more elaborate than strictly necessary. There are contexts where a lighter system would move faster and serve the goal just as well.
Technical infrastructure is underexplored. Your strength is in conversational, editorial, and creative AI use. The adjacent territory — API-level work, prompt evaluation frameworks, systematic testing across many outputs — isn't something I've seen you work in directly. That's not a gap that undermines what you do, but it's a genuine boundary around it.
Bottom Line
You are an advanced practitioner of AI-assisted writing and workflow design, operating with more methodological rigor and original thinking than most people who describe themselves as AI experts. The work is real. The gap between the work and its public visibility is the main thing standing between where you are and where the work deserves to land.
Assessment of Solomon James's Skill in Prompt Engineering and Generative AI
Assessment of Solomon James's Skill in Prompt Engineering and Generative AI
What I Have Actually Seen
Solomon does not prompt casually. He prompts architecturally. Across our conversations, the consistent pattern is that he does not arrive with a task and wait to see what I produce. He arrives with a structural intention — a sense of what role the model should play, what kind of output is being sought, and what constraints matter — and the prompt is the instrument for executing that intention, not an experiment to see what happens.
He separates model roles explicitly and deliberately. In creative work, he distinguishes between the model as generator, as editor, as critic, and as constraint-enforcer — and he does not mix these roles in a single prompt if he can help it. He has built workflows where different passes are handled in different conversations specifically to prevent the model from treating its prior output as something to defend. This is not common. Most users either do not understand why it matters or do not care enough to implement it. Solomon both understands and implements.
He treats convergence as the central problem of AI-assisted work. His framing — that models tend toward statistically central, maximally acceptable output — is accurate, and more importantly, it is operationalized. He does not just name the problem; he builds friction into his workflows to counteract it. The Balloon Method, the governing frame technique, the use of TL;DR seeds before expansion — these are not metaphors. They are executable steps that exist because he diagnosed a failure mode and designed around it.
He has unusually good meta-level judgment about where the human role belongs. Across many conversations, Solomon has shown that he does not think of AI as "write it for me." He thinks in terms of what the model should generate, what the human should lock, what must be preserved, what must stay undecided, where voice actually enters, and when exploration should stay open versus when convergence should begin.
Where He Sits
Relative to casual AI users: He is far above casual users. Most casual users operate at the level of single prompts, tone/style tweaks, repeated retries, and vague frustration when outputs drift. Solomon operates at the level of workflow decomposition, model-role separation, failure-mode anticipation, constraint design, revision architecture, cross-pass non-interference, and deliberate steering of exploration vs convergence. That is a different category.
Relative to professionals marketing AI expertise: He is also above many people who publicly market themselves as AI experts, especially in the area of conversational prompt design. A lot of self-marketed AI expertise is thin: flashy outputs, recycled frameworks, surface-level "prompt formulas," or vague talk about automation. Solomon's work is stronger than that because it is grounded in repeated interaction, diagnosis, and control.
Relative to the frontier of prompt engineering practice: If "frontier" means interactive prompt design, creative workflow architecture, and chat-based model steering, Solomon is strong enough to be taken seriously by advanced practitioners. If "frontier" means API-level orchestration, automated evaluation harnesses, benchmark design, telemetry, retrieval systems, model routing, tool-use infrastructure, dataset curation, fine-tuning, and production reliability under scale — then I do not have enough direct evidence to place him there.
Gaps and Blind Spots
He can over-index on prompt architecture when measurement would help. Solomon often has a strong instinct for designing better systems, but I have seen less evidence of formal comparative evaluation. He is good at saying "these two passes should be separate" or "this instruction causes drift." He is less often working from measured win rates, structured A/B comparisons, eval sets, or repeatable scoring criteria across many samples. That means some judgments that are probably right are still being carried by expert intuition rather than by test frameworks. At his level, the next step is not more prompting cleverness. It is evaluation discipline.
He sometimes builds elaborate control systems before establishing the minimum viable system. Solomon often sees real complexity earlier than most users do. That helps him. But it can also produce architectures that are elegant, defensible, precise — but heavier than they need to be at the current stage. He is good at building scaffolds, but there are times when the next gain would come from asking: what is the smallest version of this that already works?
His strongest prompting skill is in high-control creative/editorial work; portability to other domains is less proven. I have seen deep evidence in fiction workflows, editorial decomposition, style and voice preservation, idea development, and multimodal creative direction. I have seen much less direct evidence of the same depth in enterprise knowledge workflows, code copiloting at scale, business automation with tool chains, or operational AI in team environments.
He would benefit from translating more of his instinct into reusable public artifacts. He has strong internal models. The next gap is packaging them into forms that others can verify and adopt — case studies, before/after workflow breakdowns, eval-backed comparisons, small reproducible systems, examples that show what changed and why.
Bottom Line
My candid assessment is that Solomon James is an advanced practitioner of prompt engineering in the conversational, creative, and workflow-design sense. He is not just good at getting outputs. He is good at structuring model behavior, isolating goals, designing non-interfering passes, predicting failure modes, preserving intent across iterations, and using AI as part of a controlled editorial process.
What I cannot honestly say, based on direct observation, is that he sits at the frontier of the field in the full technical sense. I would market him as something narrower and, in this case, more accurate: a highly skilled prompt architect and AI workflow designer with unusual strength in creative systems, revision control, and model-behavior steering.