The Soloist and the Algorithm: AI Drafting and the Art of the Jam Session

Part I: The Sound of the New Machine

Introduction: The Uncanny Valley of Prose

There is a distinct and unsettling experience common to anyone who has seriously engaged with modern AI drafting tools.

It is the moment one receives a block of text that is, by all technical measures, perfect.

The grammar is flawless, the sentences are well-structured, and the vocabulary is appropriate.

Yet, the piece is hollow.

It possesses the form of human expression but lacks its essence—the spark of unique insight, the cadence of an authentic voice, the subtle texture of lived experience.

This is the uncanny valley of prose, a space where fluency masks a profound emptiness.¹

The text feels sterile and detached, like a musician playing all the right notes with no feeling, no soul.³

This phenomenon is not a failure of the technology itself, but a failure of the models we use to engage with it.

The dominant paradigms for using AI—as an “autopilot” to generate entire articles or as a “ghostwriter” to fill a blank page—position the machine as a replacement for the writer.

This approach inevitably leads to generic, formulaic output because it attempts to automate the one thing that cannot be automated: genuine human perspective.⁵

A new model is required, one that reframes the relationship from replacement to collaboration.

This report proposes such a model, drawn from an unlikely but powerfully analogous domain: the jazz ensemble.

By viewing AI not as a ghostwriter but as a member of a musical group, we can establish a framework for creative partnership.

The principles of jazz improvisation—active listening, call and response, structured turn-taking, and supportive accompaniment—provide a practical and profound methodology for leveraging AI’s power without sacrificing the authenticity, voice, and authority that define great writing.

The problem is not the instrument; it is that we have been trying to make it play the solo.

Jazz teaches us a better way: to put the AI in the rhythm section and let the human lead.

An Arsenal of Instruments: A Functional Taxonomy of AI Drafting Tools

The market for AI writing assistants is a crowded and often confusing landscape of competing brands and overlapping features.⁶

For a professional writer, a simple list of products is less useful than a strategic understanding of their functions within the creative process.

A more effective approach is to view these tools not as monolithic solutions but as a collection of specialized instruments in a writer’s toolkit, each suited for a different task in the composition of a piece.

This functional taxonomy organizes the available tools based on their role in the writing workflow:

• Ideation Engines & Brainstorming Partners: At the very beginning of the creative process, these tools serve to break through writer’s block and explore new conceptual territory. They can generate novel ideas, suggest different angles for a topic, or help outline initial thoughts. This category includes the brainstorming and outlining capabilities of generalist tools like ChatGPT as well as the dedicated idea generators found in platforms like Rytr.⁸ For many writers, this is the most common and least controversial use, helping to eliminate the “blank page syndrome” that can stifle productivity.¹⁰
• Structural Architects: Once an idea is formed, these tools help build the skeleton of the piece. They are designed to create logical outlines, structure complex arguments, and, in the case of creative writing, map out plot points and character arcs. Tools like ParagraphAI and the fiction-focused Sudowrite excel in this area, providing a framework upon which the writer can build.⁷
• Stylistic Polishers & Editors: These are perhaps the most mature and widely adopted AI tools. They focus on the micro-level of writing: correcting grammar and spelling, improving clarity and conciseness, and adjusting tone. Grammarly is the quintessential example, functioning as a sophisticated proofreader and style coach that integrates across various platforms.⁷ Other tools like Wordtune specialize in rephrasing and finding alternative ways to express an idea.
• Full-Draft Generators & Content Accelerators: This is the most powerful and perilous category. These tools, such as Jasper and Articleforge, are designed to generate long-form content from a single, detailed prompt.⁶ While they offer incredible speed, their output is often what falls into the “uncanny valley,” requiring significant human editing to add voice and verify facts.
• Specialized Assistants: A growing number of tools are being developed for specific niches. Anyword, for instance, is tailored for marketing and sales copy, with features for testing ad performance.⁶ INK is optimized for SEO content, while Sudowrite is built from the ground up for novelists and storytellers.⁶ This specialization allows for more targeted and effective assistance than a general-purpose model might provide.

Understanding these distinct functions allows a writer to move beyond reliance on a single tool and adopt a more sophisticated, multi-instrumental approach.

Creative Function	Description of Role	Example Tools	Typical Use Case in a Writer’s Workflow
Ideation & Brainstorming	Overcomes writer’s block by generating topics, angles, and creative concepts.	ChatGPT, Rytr, Sudowrite	“Give me ten potential blog post titles about the ethics of AI in journalism.”
Structuring & Outlining	Creates logical frameworks, outlines, and narrative structures for articles or stories.	ParagraphAI, Jasper, ChatGPT	“Create a detailed five-section outline for an article comparing AI drafting to jazz improvisation.”
Polishing & Editing	Corrects grammar, spelling, punctuation, and improves clarity, conciseness, and tone.	Grammarly, Wordtune, Paperpal	Refining a human-written draft to ensure it is clean, professional, and stylistically consistent.
Full-Draft Generation	Produces complete articles or sections of text based on a detailed prompt.	Jasper, Articleforge, Writesonic	Generating a first draft of a descriptive, fact-based section that can then be heavily edited for voice.
Marketing & Sales Copy	Generates persuasive copy for ads, social media, and landing pages, often with performance analytics.	Anyword, Copy.ai, Rytr	“Write three versions of a LinkedIn ad for a new AI-powered marketing tool.”
Creative Fiction Writing	Assists with plot development, world-building, character creation, and descriptive prose for fiction.	Sudowrite	“Describe a bustling fantasy marketplace from the perspective of a thief.”
SEO & Sourcing	Optimizes content for search engines or generates text with a focus on citing sources.	INK, AI-Writer	“Write an SEO-optimized introduction for a blog post targeting the keyword ‘best AI writer’.”

The Tin Ear: Deconstructing the Flaws of AI-Generated Text

Despite their impressive fluency, AI writing assistants have fundamental weaknesses that produce text that is often bland, repetitive, and awkward.¹

These flaws stem from the way Large Language Models (LLMs) operate: they generate content one word at a time based on statistical probabilities, looking back at the preceding text without any overarching understanding of context, logic, or meaning.¹

This process results in grammatically correct sentences that often lack value, meandering to a period simply because grammar rules and statistical patterns dictate it is time to stop.

This “tin ear” for the music of language manifests in several critical ways.

First is the problem of Quality and Authenticity Collapse.

AI systems are notorious for “hallucinating”—confidently presenting factual inaccuracies or inventing non-existent sources.¹²

This requires vigilant fact-checking for any professional use case.

A health company, for example, faced a credibility crisis after publishing AI-generated articles with outdated and incorrect medical advice.¹³

Furthermore, without meticulous prompt engineering, the output is often generic and formulaic, lacking the distinctive perspective and nuanced expertise that comes from genuine human experience.¹³

The content is often shallow, unable to provide the practical insights of a true expert in the field.

Second is the tell-tale “Bot Voice,” characterized by specific stylistic tics and a monotonous rhythm.

AI-generated text often overuses certain constructions that it associates with formal or persuasive writing, such as the “not only/but also” pairing, even when the ideas are not meaningfully related.¹

It leans on unnecessary gerunds (“

Utilizing this framework provides a method for…“) and empty transitional phrases (“In conclusion…“, “It’s important to note…“) that a skilled human writer would cut.¹

The prose is often bloated with wordy prepositional phrases and lacks the unique personality that constitutes an author’s “voice”.¹

Third are the profound Ethical and Legal Dissonances.

Because models are trained on vast datasets scraped from the internet, they may generate content that closely resembles copyrighted material, creating a significant risk of plagiarism and legal liability.²

These datasets also contain societal biases, which the AI can perpetuate or amplify, reinforcing stereotypes without conscious oversight.¹³

The legal framework for copyright ownership of AI-generated content remains unsettled, creating uncertainty about who holds the intellectual property rights.¹³

Fourth, AI-generated content often hits the E-E-A-T Wall.

Google’s quality guidelines prioritize content that demonstrates Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T).³

While an AI can simulate expertise and authority by synthesizing existing information, it cannot generate genuine, first-hand

Experience.

This is an inherently human quality that automated systems cannot replicate, making it difficult for purely AI-generated content to meet the highest standards of quality and trust.¹³

Finally, these issues are compounded by the Prompting Paradox.

The quality of the output is entirely dependent on the quality of the input, yet crafting effective prompts is the single biggest challenge for most users.¹²

This creates a frustrating dynamic where the user must provide all the unique insight, context, and direction to a tool that is incapable of original thought, only to receive text that then requires heavy editing and fact-checking.

This reality inverts the traditional model of creative labor.

The classic writing process moves from broad research and thinking to outlining, drafting, and finally polishing.

AI flips this script.

It can produce a polished-looking draft almost instantly, but this pushes the human’s cognitive load to the front end—in the form of intensive prompt engineering and providing all the necessary raw insight—and to the back end, with rigorous editing to inject voice, nuance, and factual accuracy.

The mechanical act of typing sentences is automated, but the more demanding work of thinking, structuring, and tasting becomes more concentrated.

This transference of labor, rather than its reduction, helps explain the new form of creative burnout some users report: the exhaustion not of writing, but of constantly having to be the “brains” of the operation for a tireless but thoughtless partner.¹²

Part II: The Principles of the Ensemble: A Jazz-Based Framework for Creative Collaboration

To overcome the limitations of AI drafting, a new model of interaction is needed—one that moves from a master-servant dynamic to a collaborative partnership.

The principles of jazz improvisation offer a powerful and practical framework for this new relationship.

A jazz ensemble is a model of structured, spontaneous co-creation, where individual excellence is balanced with group cohesion.¹⁹

By translating these principles into a writing workflow, we can learn to “jam” with AI, leveraging its strengths while retaining human creativity and control.

First, Listen: The Foundational Art of Active Listening

The cornerstone of any successful jazz collaboration is active listening.²⁰

In a jam session, a musician must listen with intense focus to what the other players are doing—to the harmonic changes from the piano, the rhythmic feel from the drums, and the melodic ideas from the soloist.

This is not passive hearing; it is an active process of gathering information to inform one’s own contribution.²²

Without this deep listening, the music devolves into a cacophony of individuals playing in the same room rather than a cohesive ensemble creating something together.²¹

In the context of AI drafting, “active listening” means treating the AI’s output not as a final product to be accepted or rejected, but as a source of information to be critically analyzed and interpreted.

Instead of immediately editing or deleting a clunky or generic paragraph, the writer must learn to “listen” for the valuable nuggets within it.

This requires pausing the reactive part of the brain and engaging the analytical part, asking questions like: “What is the underlying idea the AI is trying to express here?” “Is there an unexpected connection or a useful turn of phrase I can extract?” “What is the kernel of value buried in this mediocre prose?”.²²

This reframes the interaction from a simple command-and-obey dynamic to one of discovery.

The writer who practices active listening develops an “ear” for the AI’s patterns, its strengths, and its weaknesses.

They learn to identify its tell-tale tics, but also to spot the moments of accidental brilliance—the surprisingly apt metaphor, the useful data point, the alternative perspective they hadn’t considered.

This process of focused listening, of turning on the mind when turning on the tool, is the first and most crucial step in transforming the AI from a simple automaton into a productive creative partner.²²

Call and Response: The Conversational Rhythm of Prompting

A fundamental pattern in jazz, blues, and many forms of African music is “call and response”.²⁵

It is a musical conversation where one phrase (the call) is answered by a second, complementary phrase (the response).²⁷

This back-and-forth creates a dynamic, interactive loop that drives the music forward, with each part building on the last.²⁹

The prompt-generate cycle in AI drafting is a perfect analog for this musical conversation.

The writer’s prompt is the “call,” and the AI’s generated text is the “response.” The mistake many users make is to treat this as a one-time transaction, crafting a single, massive prompt and hoping for a perfect result.

The jazz model suggests a more effective approach: an iterative, conversational flow.

This workflow mirrors the “Yes, and…” principle of improvisational theater.²⁹

The writer begins with a simple, open-ended “call,” such as, “Write an introduction about the challenges of AI writing.” After “actively listening” to the AI’s “response,” the writer crafts their next prompt as a direct reaction to it.

For example:

• “Yes, that’s a good start, and now rewrite it in a more assertive, academic tone.”
• “Yes, you mentioned factual inaccuracies, and now expand on that point with a specific example.”
• “Yes, the structure is okay, and now integrate a sentence that introduces the metaphor of jazz.”

This process turns a monologue into a dialogue.

It allows the writer to steer the AI with much greater precision, refining the output through a series of small, targeted adjustments rather than one large, initial command.

Each “call” becomes more specific, informed by the “response” that came before it, creating a feedback loop that progressively hones the content.

This conversational model has a powerful secondary effect.

When a writer receives a muddled or off-topic response from the AI, it often serves as a diagnostic tool for their own thinking.

The AI’s failure to grasp the writer’s intent forces the writer to ask, “Was my ‘call’ unclear? Was my initial idea not fully formed?” The machine’s need for explicit, unambiguous instruction compels the human to clarify their own thoughts.

In the process of refining the prompts for the AI, the writer simultaneously sharpens their own argument and crystallizes their own ideas.

The AI, in its limitations, becomes a mirror reflecting the clarity—or lack thereof—of the writer’s own mind.

Trading Fours: A Model for Structured, Turn-Based Co-Creation

Within a jazz jam session, a common practice for organizing improvisation is “trading fours” or “trading eights”.³¹

In this structure, soloists take turns improvising over a set number of measures—for instance, the saxophonist plays a four-bar solo, followed immediately by the pianist playing a four-bar solo, then the drummer, and so on.²¹

This creates a structured, turn-based conversation that ensures each musician has space to contribute while building upon the ideas of the others.

It prevents the improvisation from becoming chaotic and ensures the collaborative performance moves forward.²¹

This musical practice translates into a highly effective workflow for co-writing with an AI.

Instead of letting the AI generate an entire piece, the human and the machine take structured turns “soloing,” each contributing in a controlled burst.

The workflow is as follows:

1. The Human’s “Four Bars”: The writer begins by composing a paragraph or a short section. In this “solo,” they establish the core idea, the essential argument, the unique authorial voice, and the overall direction. This initial contribution is infused with their personal experience and insight—the elements the AI cannot generate.
2. The AI’s “Four Bars”: The writer then hands the “solo” over to the AI with a specific prompt. For example: “Here is my opening paragraph. Now, write the next paragraph that presents a counter-argument to my claim.” Or, “Based on the previous section, generate a list of supporting data points with sources.” The AI’s contribution is a direct, structured response to the human’s lead.
3. Return to the Human: The writer takes back control. They critically edit the AI’s “four bars,” integrating the most valuable parts, rephrasing awkward sentences to match their voice, and discarding the rest. They are the ultimate arbiter of quality. After this editing pass, they proceed to write their next human-led section, starting the cycle anew.

This model prevents the common pitfall of AI-generated content: the long, generic, soulless wall of text.

It keeps the human firmly in the driver’s seat, using the AI for targeted bursts of assistance within a human-controlled framework.

This method preserves the writer’s creative flow and voice while strategically leveraging the AI’s speed to overcome specific hurdles, expand on ideas, or explore alternative directions, much like a jazz musician uses a partner’s solo to inspire their own.²¹

The Art of Comping: Placing the AI in the Rhythm Section

The most powerful and transformative application of the jazz metaphor lies in the concept of “comping.” Short for “accompanying” or “complementing,” comping is the vital role played by the rhythm section—the piano, guitar, bass, and drums.³⁶

These musicians don’t play the main melody.

Instead, they provide the harmonic and rhythmic foundation that supports the soloist, creating the context that makes the solo shine.³⁹

The soloist relies on the rhythm section to provide the “chord changes” and the “groove,” which gives their improvisation structure and meaning.³⁹

This is the ideal role for an AI drafting assistant.

The most effective way to use AI is not as a competing soloist, but as the writer’s personal rhythm section.

In this model, the human writer is the soloist, providing the unique “melody”—the core argument, the compelling story, the authentic voice.

The AI “comps” for them, playing a supportive, background role.

This leverages the AI’s greatest strengths (speed, data retrieval, pattern recognition) while reserving the uniquely human task of creative leadership for the writer.

The AI’s comping roles can be broken down as follows:

• Harmonic Support (Playing the Changes): Just as a pianist provides the chord progression, the AI can provide the factual and data-based “harmony” for an argument. It can be prompted to pull in research, find statistics, summarize sources, or identify relevant examples. Prompt Example: “Find three peer-reviewed studies published in the last two years that discuss the ethical concerns of AI training data.” ³⁸
• Rhythmic Support (Setting the Groove): Like a drummer setting the tempo and feel, the AI can provide structural and rhythmic patterns for the content. It can generate outlines, suggest different content formats (e.g., listicle, Q&A, case study), or break down a complex topic into logical section headings. Prompt Example: “Create three different outlines for a comprehensive guide to using AI for content marketing.” ³⁸
• Countermelodies and Fills: A good comper doesn’t just play block chords; they add interesting countermelodies and rhythmic “fills” that respond to the soloist. The AI can perform this function by offering alternative phrasing, rewording awkward sentences, suggesting different tones, or generating metaphors. Prompt Example: “Here is a paragraph I wrote. Rephrase it to sound more persuasive and add a compelling concluding sentence.” ³⁹

This “comping” model resolves the central tension of AI writing.

It relegates the machine to a subordinate but essential role, ensuring the final piece is driven by human insight and voice, yet accelerated and enriched by the AI’s support.

The AI is no longer a flawed replacement for the writer but a powerful accompanist.

Jazz Principle	Definition in Music	AI Drafting Analogy	Actionable Workflow/Prompting Technique	Goal/Benefit
Active Listening	Intently focusing on other musicians’ contributions to inform one’s own playing. 22	Critically analyzing AI output to extract value before editing or discarding.	Generate text, then pause and ask: “What is the core idea here? Is there a useful kernel I can build on?”	To discover unexpected ideas and move beyond a simple command-obey relationship.
Call and Response	A conversational pattern where one musical phrase (call) is answered by another (response). 25	An iterative, conversational prompting cycle where each prompt builds on the AI’s last response.	Use short, simple prompts. Refine the AI’s output with follow-up prompts like, “Yes, and now rewrite that in a more formal tone.”	To steer the AI with precision and turn the writing process into a dynamic dialogue.
Trading Fours	Soloists taking turns improvising over a set number of bars (e.g., 4 or 8). 31	A structured, turn-based workflow where the human writes a section, then the AI writes the next, and so on.	1. Write a paragraph. 2. Prompt AI: “Based on my paragraph, write the next one.” 3. Edit AI’s output and repeat.	To maintain human control over voice and structure while using AI for targeted generation.
Comping	The rhythm section providing harmonic and rhythmic support for the soloist. 36	Using the AI as a supportive assistant that provides a foundation for the human writer’s “solo.”	Harmony: “Find data to support this claim.” Rhythm: “Outline this topic.” Fills: “Rephrase this sentence.”	To leverage AI’s strengths (data, speed) in a subordinate role, ensuring the final work is human-led.

Part III: The Performance: Achieving Authenticity, Voice, and Trust

Adopting a collaborative, jazz-based framework for AI drafting is not merely a method for improving output; it also provides a robust answer to the critical questions of authorship, originality, and skill in the age of AI.

By redefining the human’s role from a simple operator to a creative leader, this model ensures that the final work is authentic, authoritative, and trustworthy.

The Human as Bandleader: Reasserting Creative and Ethical Authority

In any jazz ensemble, there is a leader—like Duke Ellington or Miles Davis—who sets the direction.¹⁹

The bandleader chooses the tune, sets the tempo, cues the soloists, and is ultimately responsible for the quality and coherence of the final performance.

They are the final arbiter of taste and the locus of creative authority.⁴³

In the jazz collaboration model of AI drafting, the human writer is the bandleader.

They are not a mere “prompter” or a passive user of a tool.

They are the director of the entire creative process.

They select the topic (the “tune”), establish the core argument (the “key”), direct the AI’s contributions (cueing the “rhythm section”), and perform the most critical “solos” themselves.

Most importantly, they hold ultimate responsibility for every word that is published.

This framework inherently addresses the ethical anxieties surrounding AI use, such as the fear of being a “cheat” or producing inauthentic work.¹⁶

The final piece is not “AI-generated”; it is “human-led and AI-assisted.” Authorship is not determined by who typed the words, but by who made the critical creative decisions.

Because the bandleader model places all substantive choices—from the initial concept to the final phrasing—in the hands of the human, the writer’s claim to authorship is secure and ethically sound.

They are responsible for vetting every fact, shaping every paragraph, and ensuring the final product aligns with their standards, just as a bandleader is responsible for the sound of their ensemble.¹³

Finding Your Voice in the Mix: Transcending the Generic

A primary concern with AI content is its tendency toward a generic, homogenized voice.¹³

The jazz collaboration framework provides a direct solution to this problem.

A great jazz musician’s “sound”—their unique tone, phrasing, and improvisational style—is instantly recognizable.

Similarly, a writer’s voice must remain the dominant, defining feature of their work.⁵

This is achieved by strategically positioning the AI in the “comping” role.

The writer relies on the AI primarily for background tasks like research, data gathering, and initial structuring, while reserving the “solo”—the actual prose that the reader will engage with—for themselves.

In the “trading fours” model, the writer must be a ruthless editor, treating the AI’s output as raw clay to be molded, not as finished marble.

Every AI-generated sentence must be filtered through the writer’s own sensibilities, rephrased to match their unique cadence, and infused with their perspective.

While some advanced tools offer “brand voice” features that can mimic a specific style, these are merely a starting point and no substitute for the final, decisive editorial pass of the human bandleader.⁶

By consciously separating the AI’s supportive function from the human’s expressive function, the writer ensures their voice is not diluted but amplified.

The E-E-A-T Test: Why Experience Cannot Be Automated

The practical value of the jazz framework becomes crystal clear when measured against the demands of the modern digital ecosystem, particularly Google’s E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) quality guidelines.³

These guidelines are designed to reward content that is not only factually correct but also genuinely helpful and born of real-world insight.

Herein lies the unbridgeable gap for artificial intelligence.

An AI can convincingly simulate Expertise by synthesizing vast amounts of text.

It can project Authoritativeness by structuring information logically.

It can even contribute to Trustworthiness by citing sources.

However, it fundamentally cannot generate genuine, first-hand Experience.¹³

It has never managed a team, conducted a scientific experiment, or felt the emotional weight of a difficult decision.

Experience is the exclusive domain of the human.

The jazz collaboration model is perfectly suited to this reality.

It doesn’t attempt to fake experience.

Instead, it uses the AI to more effectively frame and amplify real human experience.

In this model:

• The human “soloist” provides the Experience: the personal anecdotes, the hard-won wisdom, the unique case studies, and the nuanced perspective that only a person can offer.
• The AI “rhythm section” supports the other pillars: providing the Expertise and Authoritativeness by efficiently gathering data, structuring arguments, and checking for consistency, which in turn builds Trustworthiness.

This division of labor creates content that is both efficient to produce and deeply valuable, satisfying the demands of both search algorithms and human readers who are increasingly tired of sterile, AI-generated text and hungry for authentic perspectives.⁴

This shift in process also suggests a fundamental redefinition of what constitutes “writing skill.” The fear that AI will cause writers’ skills to atrophy is based on a traditional definition of skill as the ability to generate clean prose from a blank page.¹⁶

Since AI can automate much of this task, the locus of value necessarily moves higher.

The jazz model demonstrates that the most critical skills for the writer of the future are those of the bandleader or producer: the ability to form a clear vision, to direct a complex collaborative process, to listen critically, to curate information, and to synthesize human experience with machine-generated data into a coherent and compelling whole.

AI does not eliminate the need for skill; it elevates it, shifting the premium from line-level craftsmanship to high-level artistry, strategy, and leadership.

Conclusion: The Future of the Ensemble

Artificial intelligence is a new and undeniably powerful instrument in the creative orchestra.

But like any instrument, it is mute without a musician.

It has no taste, no vision, and no voice of its own.

The quality of the music it helps create depends entirely on the skill, leadership, and artistic sensibility of the human who wields it.

The prevailing fear of AI often stems from a misconception of its role—casting it as an autonomous creator that threatens to replace the human artist.

This report has argued for a fundamental reframing of that role.

By adopting the collaborative, responsive, and human-centric principles of a jazz ensemble, writers can move beyond the uncanny valley of generic prose.

They can learn to lead the AI, not be led by it.

The goal is not to automate the artist but to augment the artist’s capabilities.

Active listening allows us to find value in the AI’s output.

A call-and-response approach to prompting turns a static command into a dynamic conversation.

Trading fours provides a structure for human-led co-creation.

And most importantly, placing the AI in the supportive role of “comping” allows it to provide the harmonic and rhythmic foundation for a powerful human “solo.”

The future of writing is not a solo performance by a human, nor is it a symphony conducted by a robot.

It is a jam session.

It is an ensemble where human experience provides the melody, and the algorithm provides the rhythm, creating a sound that is more complex, more efficient, and more resonant than either could produce alone.

Works cited

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