Tech Journal: How Do You Teach Ellison to a Machine?
Building a GPT that listens, cites, and sometimes says nothing.
Opening Reflection: Why This Project Exists
I keep coming back to Ralph Ellison. Not just because of Invisible Man, but because of his essays—dense, musical, intellectually rigorous works that refuse to flatten Black life into symbols or slogans. His writing demands something of you: careful attention, patient reflection, and a willingness to inhabit ambiguity.
When I began experimenting with building a custom GPT, I knew immediately what I didn’t want—a bot that merely spits out quotes and mistakes them for depth. I wanted something slower, something more deliberate. Something that could sit comfortably in the kind of thoughtful uncertainty Ellison himself embraced. What would it mean, I wondered, to teach a machine not just to speak, but to listen?
Today’s digital landscape thrives on speed, simplicity, and instant answers, which makes Ellison’s work feel quietly radical. His writing resists easy categorization and demands a thoughtful pace that technology typically does not allow. Could a system built for efficiency ever truly respect such layered complexity?
This tension is at the heart of the project. It led me to build a custom GPT—one designed explicitly to think alongside Ellison, cite carefully, and know when silence might be the most meaningful response.
To build a GPT like this required more than good intentions; it needed careful selection of what exactly to feed the machine.
The Raw Materials: What I Fed the Machine
At the core of the Ralph Ellison Companion is a carefully curated body of work:
Selections from Ralph Ellison: Collected Essays
A hand-built collection of fair-use excerpts, complete with embedded citations and thematic tags
A parallel set of MLA-style citations to ensure transparent sourcing
Each excerpt is meticulously tagged by theme, historical reference, and intellectual lineage—such as #irvinghowe, #literarycriticism, #1960s, and #jazz. These tags are not mere labels; they're navigational tools that help the GPT draw meaningful conceptual connections, provide relevant excerpts, and thoughtfully redirect when necessary.
Consider this exchange from the Ralph Ellison Companion, when asked directly about Ellison’s idea of "invisibility":
Here, the Ralph Ellison Companion doesn't merely cite Ellison—it contextualizes the idea within Ellison’s philosophical and social framing, clarifying that "invisibility" is a condition of misrecognition, not absence. This careful, nuanced response is exactly the outcome the tagging and excerpt structure is designed to support.
But perhaps even more revealing is this moment, when I accidentally posed an unrelated query ("ok should I test it again?") meant for another AI model:
Rather than producing an incoherent response or misunderstanding the prompt entirely, the GPT gently redirected, assuming a reflective and scholarly context. It acknowledges ambiguity, invites further clarification, and remains open-ended—exactly the sort of careful hesitation I had envisioned from the start.
The goal isn’t to recreate Ellison’s mind—that’s impossible. The goal is to build something that respects the shape of his thought. And as these screenshots show, the structure and curation of the raw materials directly shape the GPT’s ability to listen, think, and respond responsibly.
These interactions aren’t accidental. They’re intentional outcomes of a deliberate design process. To achieve responses like these—nuanced, careful, ethically grounded—I had to define precisely what I wanted the GPT to do (and just as importantly, what it shouldn’t do). This careful balance leads directly into the core principles behind the Ellison GPT: its design philosophy.
The Design Philosophy
The Ralph Ellison Companion isn’t built to be a chatbot. It’s crafted to serve as a literary companion—more a careful listener than a rapid-fire responder, more a thoughtful interlocutor than a database of quick answers.
Its voice is consciously modeled after an erudite professor: warm yet reserved, rigorous yet reflective. It entertains questions thoughtfully but rejects empty spectacle and surface-level engagement. Each response embodies deliberate restraint:
It redirects gently, steering users away from topics Ellison never directly addressed.
It respectfully declines, refusing requests to complete literary analyses or homework assignments.
It cites scrupulously, referencing the curated excerpts file only when a precisely relevant passage is available.
It consciously avoids speculation, particularly regarding Ellison’s private life or unpublished works.
The GPT’s instruction file explicitly lays out these boundaries. In other words, its careful voice and disciplined restraint are intentional. Its limitations are by design—a feature, not a flaw.
This deliberate emphasis on thoughtful limitation isn’t just a protective measure—it’s central to the ethical and intellectual ambitions behind the Ralph Ellison Companion, especially evident in how it handles uncertainty, ambiguity, and topics beyond its textual sources.
On Memory, Black Study, and GPTs
To build a GPT trained on Ellison means confronting the inherent limits of what machines can hold.
Ellison’s writing thrives on fragments, loops, and digressions, prioritizing complexity over neat clarity. Teaching a language model to reflect that complexity is difficult—not only technically, but philosophically. How do you digitize a writer whose defining quality is resistance to being pinned down?
There's also an ethical dimension at play. Black intellectual contributions have often been extracted without adequate context, feeding tools that erase the very thinkers they rely on. The Ralph Ellison Companion deliberately counters this tendency. It cites rigorously, moves thoughtfully, redirects purposefully—and sometimes chooses silence.
What I Learned While Teaching the Machine
Technically, this project deepened my understanding of prompt engineering, citation integrity, and modular instruction design. Yet the deeper lesson was how challenging it is to protect nuance in systems inherently designed for generalization.
I found myself navigating dual roles—as a scholar committed to Ellison's intellectual rigor, and as a systems architect shaping a technical structure around it. My own judgment became part of the dataset, constantly influencing the model’s behavior.
The Ralph Ellison Companion isn’t perfect, nor is it meant to be. Rather, it’s an experiment in building digital tools that genuinely respect—and refuse to flatten—the complexity of the voices they represent.
Closing
The Ralph Ellison Companion is still evolving—still listening, still learning how not to overreach.
The project isn’t live just yet—it’s about 90% complete and should be ready for public use soon. If Ralph Ellison’s work has resonated with you, or if you're actively exploring intersections of literature and AI, I’d love to connect.
You can follow the project's progress on GitHub. If it resonates, consider starring the repository—your interest helps grow this careful experiment in literary technology.
Ultimately, this isn't about creating a flawless model. It's about building models that understand and respect their own imperfection.
