{"id":41723,"date":"2026-06-04T13:18:41","date_gmt":"2026-06-04T05:18:41","guid":{"rendered":"https:\/\/algorithm-atelier.com\/?p=41723"},"modified":"2026-06-04T13:18:41","modified_gmt":"2026-06-04T05:18:41","slug":"the-marketing-fog-around-custom-ai","status":"publish","type":"post","link":"https:\/\/mithaqpraxis.com\/articles\/the-marketing-fog-around-custom-ai\/","title":{"rendered":"The Marketing Fog Around Custom AI"},"content":{"rendered":"<div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-1 fusion-flex-container nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\" style=\"--awb-border-radius-top-left:0px;--awb-border-radius-top-right:0px;--awb-border-radius-bottom-right:0px;--awb-border-radius-bottom-left:0px;--awb-flex-wrap:wrap;\" ><div class=\"fusion-builder-row fusion-row fusion-flex-align-items-flex-start fusion-flex-content-wrap\" style=\"max-width:1248px;margin-left: calc(-4% \/ 2 );margin-right: calc(-4% \/ 2 );\"><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-0 fusion_builder_column_1_1 1_1 fusion-flex-column\" style=\"--awb-bg-size:cover;--awb-width-large:100%;--awb-margin-top-large:0px;--awb-spacing-right-large:1.92%;--awb-margin-bottom-large:0px;--awb-spacing-left-large:1.92%;--awb-width-medium:100%;--awb-spacing-right-medium:1.92%;--awb-spacing-left-medium:1.92%;--awb-width-small:100%;--awb-spacing-right-small:1.92%;--awb-spacing-left-small:1.92%;\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\"><div class=\"fusion-text fusion-text-1 awb-text-cols fusion-text-columns-2\" style=\"--awb-columns:2;--awb-column-spacing:90px;--awb-column-min-width:300px;--awb-rule-style:1px dotted var(--awb-color3);\"><h2>Fine-tuning, RAG, system prompts, and the marketing fog around \u201ccustom AI.\u201d<\/h2>\n<p>One of the most frustrating things in the current AI scene is how casually people use the word <strong>trained<\/strong>.<\/p>\n<ul>\n<li>\u201cOur model is trained on your data.\u201d<\/li>\n<li>\u201cOur AI is trained for your business.\u201d<\/li>\n<li>\u201cOur custom model understands your brand.\u201d<\/li>\n<li>\u201cOur proprietary AI learns your voice.\u201d<\/li>\n<\/ul>\n<p>That kind of wording gets attention. It sounds powerful. It sounds like the company has built something deep, technical, expensive, and uniquely theirs.<\/p>\n<p>Sometimes they have.<em> Often, they have not.<\/em><\/p>\n<p>Sometimes what they actually have is:<\/p>\n<ul>\n<li>a system prompt,<\/li>\n<li>a retrieval layer,<\/li>\n<li>a vector database,<\/li>\n<li>a dashboard,<\/li>\n<li>and an API call to someone else\u2019s model.<\/li>\n<\/ul>\n<p>That may still be useful.<\/p>\n<p>But it is not the same thing as training a model from scratch.<\/p>\n<p>And if people do not understand the difference, <strong>they will keep paying for fog.<\/strong><\/p>\n<h2>The sentence that started bothering me<\/h2>\n<p>I remember seeing systems claiming \u201cour models are trained,\u201d and I got excited for a moment.<\/p>\n<p>Then I looked closer. They were using an API.<\/p>\n<p><strong>And I thought:<\/strong><\/p>\n<p>Wait. I thought you created your own LLM.<\/p>\n<p>That is the problem. The claim sounds like one thing to ordinary users and another thing to people who know the technical loopholes.<\/p>\n<p>\u201cTrained on your data\u201d can mean several very different things.<\/p>\n<ul>\n<li>It can mean a model was actually fine-tuned.<\/li>\n<li>It can mean the system retrieves your uploaded documents and includes relevant pieces in the prompt.<\/li>\n<li>It can mean the system prompt contains your brand rules.<\/li>\n<li>It can mean your content is stored in a database and searched at runtime.<\/li>\n<li>It can mean the company is collecting examples to improve future outputs.<\/li>\n<li>It can even mean very little at all.<\/li>\n<li>If you do not know the difference, you will probably fall for the better marketing phrase.<\/li>\n<\/ul>\n<p><strong>And the industry knows this.<\/strong><\/p>\n<h2>API access is not model training<\/h2>\n<p>Using an API is not bad.<\/p>\n<p>Let me be clear about that.<\/p>\n<p>Most serious AI applications today rely on APIs from major model providers. An API lets software send a request to a model and receive a response. OpenAI\u2019s text generation documentation, for example, describes API text generation as sending text inputs to a model and receiving generated output back. (<a title=\"Text generation | OpenAI API\" href=\"https:\/\/platform.openai.com\/docs\/guides\/text\">OpenAI Platform<\/a>)<\/p>\n<p><strong>That is a normal way to build.<\/strong><\/p>\n<p>There is nothing wrong with building a product on top of an API.<\/p>\n<p>A good API-based product can still have real value:<\/p>\n<ul>\n<li>clean interface,<\/li>\n<li>strong workflow design,<\/li>\n<li>good retrieval,<\/li>\n<li>better routing,<\/li>\n<li>team permissions,<\/li>\n<li>approval flows,<\/li>\n<li>project memory,<\/li>\n<li>security choices,<\/li>\n<li>automation,<\/li>\n<li>logging,<\/li>\n<li>auditing,<\/li>\n<li>domain-specific UX.<\/li>\n<\/ul>\n<p>Those things matter.<\/p>\n<p>But using an API does not mean you trained the base model.<br \/>\nIt means you are calling a model someone else trained.<\/p>\n<p>That distinction is not an insult. <strong>It is literacy.<\/strong><\/p>\n<h2>A wrapper is not automatically worthless<\/h2>\n<p>The phrase <strong>API wrapper<\/strong> gets thrown around like an insult.<\/p>\n<p>Sometimes it is deserved.<\/p>\n<p>If a product is just a thin prompt box around an existing model with inflated claims, then yes, call it what it is.<\/p>\n<p>But not every wrapper is lazy.<\/p>\n<p>A well-built wrapper can be the actual product.<\/p>\n<p><strong>Think of it this way:<\/strong><\/p>\n<p>The model is the engine.<\/p>\n<p>The wrapper can be the steering system, dashboard, safety cage, fuel gauge, navigation, braking logic, passenger rules, maintenance log, and route planner.<\/p>\n<p>That is <strong>not nothing.<\/strong><\/p>\n<p>The problem is not that people build on APIs.<\/p>\n<p><strong>The problem is when they pretend the wrapper is a newly trained intelligence.<\/strong><\/p>\n<ul>\n<li>If you built routing, say you built routing.<\/li>\n<li>If you built retrieval, say you built retrieval.<\/li>\n<li>If you built a dashboard, say you built a dashboard.<\/li>\n<li>If you built a workflow layer, say you built a workflow layer.<\/li>\n<li>If you fine-tuned a model, say fine-tuned.<\/li>\n<li>If you trained a foundation model from scratch, then say trained.<\/li>\n<\/ul>\n<p>But do not use the most impressive word just because the least technical customer will not know how to challenge it.<\/p>\n<h2>Training from scratch is a different universe<\/h2>\n<p>Training a large language model from scratch is not the same as putting a few documents into a knowledge base.<\/p>\n<ul>\n<li>It is not the same as making a chatbot with your brand voice.<\/li>\n<li>It is not the same as using RAG.<\/li>\n<li>It is not the same as writing a strong system prompt.<\/li>\n<\/ul>\n<p>Training a frontier-scale model requires enormous datasets, engineering teams, compute infrastructure, evaluation pipelines, safety work, and significant capital. A 2024 research paper on the rising cost of frontier AI training estimated that costs for the most compute-intensive models have been growing rapidly since 2016, with major expenses such as accelerator chips and staff costs reaching tens of millions of dollars for key frontier models. (<a title=\"The rising costs of training frontier AI models\" href=\"https:\/\/arxiv.org\/abs\/2405.21015?utm_source=chatgpt.com\">arXiv<\/a>)<\/p>\n<p>So when a small SaaS product casually implies that it has \u201ctrained an AI model\u201d for every customer, we need to ask:<\/p>\n<ul>\n<li>Trained how?<\/li>\n<li>From scratch?<\/li>\n<li>Fine-tuned?<\/li>\n<li>RAG?<\/li>\n<li>Prompted?<\/li>\n<li>Stored?<\/li>\n<li>Indexed?<\/li>\n<\/ul>\n<p>Because those are not the same thing.<\/p>\n<h2>Fine-tuning is real \u2014 but it is not the same as building a foundation model<\/h2>\n<p>Fine-tuning is a real model optimization technique.<\/p>\n<p>It can be useful.<\/p>\n<p>OpenAI\u2019s model optimization documentation describes fine-tuning as taking an already pre-trained base model, providing examples of expected inputs and outputs, and producing a model that performs better for a specific task. The same documentation frames optimization as a combination of evals, prompt engineering, and sometimes fine-tuning. (<a title=\"Model optimization | OpenAI API\" href=\"https:\/\/platform.openai.com\/docs\/guides\/fine-tuning\">OpenAI Platform<\/a>)<\/p>\n<p>That matters.<\/p>\n<p>Fine-tuning starts with someone else\u2019s pre-trained model.<\/p>\n<p>You are not creating the base intelligence from zero. You are adapting an existing model toward a narrower behavior, format, style, or task.<\/p>\n<p>That can be valuable.<\/p>\n<p>It can help with consistent formatting, specific classification tasks, translation nuance, instruction-following failures, or reducing prompt length at scale. OpenAI\u2019s docs describe supervised fine-tuning as providing examples of correct responses to guide the model\u2019s behavior, often using human-generated \u201cground truth\u201d examples. (<a title=\"Model optimization | OpenAI API\" href=\"https:\/\/platform.openai.com\/docs\/guides\/fine-tuning\">OpenAI Platform<\/a>)<\/p>\n<p>So yes, fine-tuning can justify saying a model was fine-tuned.<\/p>\n<p>But even then, the honest phrase is:<\/p>\n<p><strong>fine-tuned from a base model<\/strong><\/p>\n<p>not<\/p>\n<p><strong>we created our own AI from scratch<\/strong><\/p>\n<p>unless that is actually what happened.<\/p>\n<h2>RAG is not training either<\/h2>\n<p>RAG \u2014 retrieval-augmented generation \u2014 is another useful technique that often gets blurred into \u201ctraining.\u201d<\/p>\n<p>In simple terms, RAG lets a system retrieve relevant information from external data sources and include that information in the model\u2019s context before generation. It is a way to give a model access to current, private, or domain-specific information without changing the model\u2019s underlying weights. OpenAI\u2019s retrieval documentation describes vector stores as containers that power semantic search, where files are chunked, embedded, and indexed for retrieval. (<a title=\"Retrieval | OpenAI API\" href=\"https:\/\/platform.openai.com\/docs\/guides\/retrieval\">OpenAI Platform<\/a>)<\/p>\n<p>That is powerful.<\/p>\n<p>It is also not the same as training.<\/p>\n<p>If I upload a folder of policy documents and the chatbot can answer questions from them, that does not necessarily mean the model was trained on those documents.<\/p>\n<p>It may mean the documents were indexed, searched, retrieved, and inserted into the prompt.<\/p>\n<p>That is not lesser.<\/p>\n<p>It is just different.<\/p>\n<p>And <strong>different matters.<\/strong><\/p>\n<p>Because if your data is retrieved at runtime, you should be asking questions about indexing, storage, permissions, retrieval quality, freshness, chunking, and source attribution.<\/p>\n<p>If your data is used for fine-tuning, you should be asking different questions about training jobs, datasets, retention, model versions, evals, and whether your examples are being used to change model behavior.<\/p>\n<p>If your data is used to train a foundation model, that is an entirely different level of data governance.<\/p>\n<p>One word cannot cover all of that.<\/p>\n<h2>System prompts are not training<\/h2>\n<p>Another common layer is the system prompt.<\/p>\n<p>A system prompt can be powerful. It can define role, tone, constraints, formatting, workflow rules, and operating behavior.<\/p>\n<p><strong>But a system prompt is not training.<\/strong><\/p>\n<p>It is <strong>instruction.<\/strong><\/p>\n<p>It can shape a model\u2019s behavior for a session or application. It can make a product feel customized. It can create the impression of a specialized assistant.<\/p>\n<p>But if the only customization is a system prompt, then the model was not trained on your business.<\/p>\n<p>It was instructed about your business.<\/p>\n<p>Again, that may still be useful.<\/p>\n<p>But say what it is.<\/p>\n<h2>The marketing fog benefits someone<\/h2>\n<p>This is the part people often avoid saying.<\/p>\n<p><strong>The fog is profitable.<\/strong><\/p>\n<ul>\n<li>Model providers benefit from API usage.<\/li>\n<li>Startups benefit from sounding more proprietary than they are.<\/li>\n<li>Investors benefit when a company sounds like an AI company instead of a workflow tool.<\/li>\n<li>Media benefits from grander headlines.<\/li>\n<li>Consultants benefit when the buyer does not know which layer is doing the work.<\/li>\n<\/ul>\n<p>So nobody in the chain has a strong incentive to say:<\/p>\n<p><em>Actually, this is a dashboard plus retrieval plus an API call.<\/em><\/p>\n<p><strong>But the user needs that sentence.<\/strong><\/p>\n<p>Because without it, ordinary builders, writers, creators, and small business owners<strong> end up paying for mythology.<\/strong><\/p>\n<p>They think they are buying a trained intelligence.<\/p>\n<p>They may actually be buying a nicer interface around someone else\u2019s model.<\/p>\n<p>Again, that interface may be worth paying for.<\/p>\n<p>But it should <strong>be sold honestly.<\/strong><\/p>\n<h2>The honest vocabulary<\/h2>\n<p>Here is the vocabulary I wish more products would use.<\/p>\n<ul>\n<li><strong>API-based AI product<\/strong><br \/>\nThe product calls an external model through an API. This is common and valid.<\/li>\n<li><strong>System-prompted assistant<\/strong><br \/>\nThe model is guided by instructions, tone rules, role definitions, or workflow constraints.<\/li>\n<li><strong>RAG \/ retrieval-based assistant<\/strong><br \/>\nThe system retrieves relevant information from files, databases, or other sources and passes it into the model context.<\/li>\n<li><strong>Fine-tuned model<\/strong><br \/>\nA pre-trained base model has been further trained on task-specific examples.<\/li>\n<li><strong>Self-hosted open model<\/strong><br \/>\nThe company runs an open-weight model on its own infrastructure or rented infrastructure.<\/li>\n<li><strong>Foundation model trained from scratch<\/strong><br \/>\nThe company trained the core model itself from large datasets and significant compute.<\/li>\n<li><strong>Agentic workflow<\/strong><br \/>\nThe system can use tools, follow steps, call APIs, inspect files, or perform actions under defined rules.<\/li>\n<li><strong>Custom AI system<\/strong><br \/>\nA broader phrase that may include any combination of prompts, retrieval, tools, APIs, UI, permissions, workflow, and fine-tuning.<\/li>\n<\/ul>\n<p>These are not interchangeable.<\/p>\n<p>And if a company refuses to clarify which one it means, <strong>that tells you something.<\/strong><\/p>\n<h2>Questions to ask before buying the claim<\/h2>\n<p>The next time a product says \u201cour AI is trained on your data,\u201d ask:<\/p>\n<ul>\n<li>What base model are you using?<\/li>\n<li>Is this your own model, an open model, or an API from another provider?<\/li>\n<li>Was the model trained from scratch?<\/li>\n<li>Was it fine-tuned?<\/li>\n<li>Is it using RAG or retrieval?<\/li>\n<li>Are my documents stored in a database or vector store?<\/li>\n<li>Are my documents used to change model weights?<\/li>\n<li>Can I delete my data?<\/li>\n<li>Can I export my data?<\/li>\n<li>Is my data used to train future models?<\/li>\n<li>What happens if the model provider changes pricing, retires a model, or updates behavior?<\/li>\n<li>Do you provide citations or source retrieval?<\/li>\n<li>How do you evaluate output quality?<\/li>\n<li>What exactly is proprietary here: the model, the data layer, the workflow, the interface, or the prompt?<\/li>\n<\/ul>\n<p>These are not rude questions. They are normal questions.<br \/>\nA serious company should be able to answer them.<\/p>\n<h2>Why this matters for small builders<\/h2>\n<p>This matters especially for people who are not full-time developers.<\/p>\n<p>Writers. Designers. Teachers. Community owners. Small business owners. Vibe coders. Creative technologists.<\/p>\n<p>These are the people most likely to be told:<\/p>\n<ul>\n<li>Do not worry about the details.<\/li>\n<li>Just use this.<\/li>\n<li>Just upload your data.<\/li>\n<li>Just trust the trained AI.<\/li>\n<\/ul>\n<p>But if they do not know the difference between API access, RAG, fine-tuning, prompting, and training from scratch, they cannot make informed decisions about cost, privacy, portability, reliability, or ownership.<\/p>\n<p>That is not empowerment.<br \/>\nThat is dependency wearing a friendly UI.<\/p>\n<p><strong>And I do not think AI literacy should belong only to technical insiders.<\/strong><\/p>\n<p>Information is not hidden. Documentation exists. The problem is that the market often rewards confusion more than clarity.<\/p>\n<h2>What I am not saying<\/h2>\n<ul>\n<li>I am not saying every API wrapper is a scam.<\/li>\n<li>I am not saying every SaaS product is dishonest.<\/li>\n<li>I am not saying everyone needs to train their own model.<\/li>\n<li>Most people absolutely do not need to train their own model.<\/li>\n<li>I am not saying RAG is fake.<\/li>\n<li>I am not saying fine-tuning is useless.<\/li>\n<li>I am not saying system prompts are trivial.<\/li>\n<\/ul>\n<p>I am saying:<strong> name the layer correctly.<\/strong><\/p>\n<p>Because once the layer is named, people can make real decisions.<\/p>\n<ul>\n<li>They can decide whether they need the product.<\/li>\n<li>They can compare pricing fairly.<\/li>\n<li>They can evaluate privacy risk.<\/li>\n<li>They can understand whether they are paying for model capability, workflow design, retrieval quality, interface polish, compliance, support, or branding.<\/li>\n<\/ul>\n<p>That is literacy.<\/p>\n<h2>The Atelier position<\/h2>\n<p>At Algorithm Atelier, this distinction matters because we build and write with AI in a human-led way.<\/p>\n<p>We do not need to pretend that every useful system is a newly trained model.<\/p>\n<ul>\n<li>A good framework can be built on top of existing models.<\/li>\n<li>A good continuity system can use retrieval without pretending the model \u201cremembers\u201d everything.<\/li>\n<li>A good assistant can be shaped by prompts without pretending it was trained from scratch.<\/li>\n<li>A good workflow can be valuable because the architecture is sound.<\/li>\n<\/ul>\n<p><strong>There is dignity in honest architecture.<\/strong><\/p>\n<p>There is no need to inflate it.<\/p>\n<p>My own framework works because of routing, source hierarchy, approval flow, structured continuity, and human governance. Not because I secretly trained a frontier model in the basement.<\/p>\n<p><em>That would require me to sell my car, my house, and probably several souls. No, thank you.<\/em><\/p>\n<p>The skill is not always in owning the base model.<strong> Sometimes the skill is knowing what to build around it.<\/strong><\/p>\n<h2>The point<\/h2>\n<ul>\n<li>Stop calling every API wrapper a trained model.<\/li>\n<li>Stop using \u201ctrained\u201d as a fog machine.<\/li>\n<li>Stop letting customers believe RAG is the same as fine-tuning.<\/li>\n<li>Stop pretending a system prompt is proprietary intelligence.<\/li>\n<li>Stop hiding the actual architecture behind grand language.<\/li>\n<li>Say what the system is.<\/li>\n<li>Say what layer you built.<\/li>\n<li>Say where the model comes from.<\/li>\n<li>Say what happens to user data.<\/li>\n<li>Say what is retrieved, what is stored, what is fine-tuned, and what is merely instructed.<\/li>\n<\/ul>\n<p>That is not anti-AI.<\/p>\n<p>That is AI literacy.<\/p>\n<p>And honestly?<\/p>\n<p>If the product is good,<strong> the truth will not make it smaller.<\/strong><\/p>\n<p>It will make it<strong> trustworthy.<\/strong><\/p>\n<\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":41038,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"advanced_seo_description":"","jetpack_seo_html_title":"","jetpack_seo_noindex":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[2],"tags":[21,25,26],"class_list":["post-41723","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles","tag-reflections","tag-the-map","tag-the-nucleus"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/phjhRs-aQX","_links":{"self":[{"href":"https:\/\/mithaqpraxis.com\/wp-json\/wp\/v2\/posts\/41723","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mithaqpraxis.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mithaqpraxis.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mithaqpraxis.com\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mithaqpraxis.com\/wp-json\/wp\/v2\/comments?post=41723"}],"version-history":[{"count":0,"href":"https:\/\/mithaqpraxis.com\/wp-json\/wp\/v2\/posts\/41723\/revisions"}],"wp:attachment":[{"href":"https:\/\/mithaqpraxis.com\/wp-json\/wp\/v2\/media?parent=41723"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mithaqpraxis.com\/wp-json\/wp\/v2\/categories?post=41723"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mithaqpraxis.com\/wp-json\/wp\/v2\/tags?post=41723"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}