How to Optimize for Open-Source LLMs

Step 1: Understand How Open-Source LLMs Learn About Brands

Open-source LLMs learn about your brand in one way: from the training data they are exposed to before the model weights are frozen. Unlike ChatGPT (which can browse the web) or Perplexity (which retrieves sources in real time), a deployed open-source model's brand knowledge is static — it knows what it learned during training and nothing after. This means the quality, accuracy, and authority of your brand information on the open web at training time is the only lever you have.

Major open-source models train on massive web crawls: Common Crawl, web archives, Wikipedia, GitHub, academic papers, forums, and curated datasets. The key insight is that these are largely the same sources that feed closed-source model training. If your brand has strong, authoritative web presence, you benefit across all models. If your web presence is thin, you are invisible to all of them.

Step 2: Audit Your Training Data Footprint

Before optimising, understand what the training data contains about your brand. Run a training data footprint audit:

1. Test the models directly. Ask DeepSeek, Llama (via a hosted endpoint), and Qwen: "What is [your brand]?", "What are the best [your category] tools?", "Compare [your brand] vs [competitor]." Record every response — what is mentioned, what is accurate, what is outdated.
2. Check Common Crawl. Common Crawl (commoncrawl.org) is the largest public web crawl dataset and a primary training data source. Search for your domain and brand mentions to understand what the training data likely contains about you.
3. Audit Wikipedia. Wikipedia is heavily weighted in training data for all major models. If your brand has a Wikipedia page, it is likely the single most influential source of brand information in open-source LLMs. If you do not have one and are notable enough, this is a high-priority gap.
4. Check GitHub and technical platforms. Open-source model training data overrepresents technical sources. If your brand has GitHub repositories, Stack Overflow presence, or technical documentation, these contribute disproportionately to your training data footprint.

Step 3: Strengthen Your Pre-Training Content

Since you cannot update an already-trained model, your strategy must focus on ensuring the next training data snapshot captures strong brand information. Prioritise:

Authoritative, well-linked pages. Training data curation often weights pages by link authority, similar to PageRank. Pages with many inbound links from authoritative domains are more likely to be included and weighted in training data. Earn links through original research, data publications, and industry coverage.

Wikipedia accuracy. If you have a Wikipedia page, ensure it is accurate, up-to-date, and comprehensive. If you do not have one and meet notability criteria, work toward one through verifiable media coverage and third-party references. Do not edit your own Wikipedia page — this violates Wikipedia policy and can backfire.

Consistent entity data. Your brand name, description, category, founding date, headquarters, and key attributes should be identical across your website, Crunchbase, LinkedIn, Wikipedia, and industry directories. Inconsistency in training data creates confused model knowledge.

Factually dense content. Training data extraction favours content with specific, verifiable facts over vague marketing copy. Include concrete data points: founding year, employee count, customer numbers, product specifications, pricing tiers. These facts become the basis of what models "know" about your brand.

Step 4: Leverage the Open-Source Ecosystem

Open-source LLM training data overrepresents content from the open-source ecosystem. Brands that participate in this ecosystem have a visibility advantage:

• GitHub presence: If relevant, maintain active, well-documented GitHub repositories. README files, documentation, and code comments are all training data.
• Hugging Face: If you produce AI models, datasets, or tools, publish them on Hugging Face with comprehensive model cards and documentation.
• Technical documentation: Detailed, publicly accessible technical docs are disproportionately represented in training data. Invest in documentation quality as a brand visibility strategy.
• Technical blog posts: In-depth technical content on your engineering blog is more likely to be captured in training data than marketing content on your corporate blog.

Step 5: Prepare for RAG Deployments

While base open-source models rely on parametric knowledge, many enterprise deployments add RAG (retrieval-augmented generation) on top — connecting the model to company knowledge bases, web search, or document repositories. This means your content needs to work for both scenarios:

• For base model visibility: Focus on training-data-quality content (authoritative, well-linked, widely cited).
• For RAG-augmented visibility: Ensure your content is accessible to web crawlers, well-structured for passage extraction, and optimised for semantic retrieval — the same principles as optimising for Perplexity.

Covering both bases ensures your brand is visible regardless of how the open-source model is deployed.

Step 6: Monitor Across Models with Presenc AI

Presenc AI monitors your brand visibility across both open-source models (DeepSeek, Qwen) and closed-source platforms (ChatGPT, Claude, Gemini, Perplexity). The platform identifies where your visibility differs between model families, revealing gaps you would not detect by monitoring only one ecosystem. As open-source models release new versions — each with updated training data — Presenc tracks whether your visibility improves, stays flat, or regresses, giving you a continuous feedback loop on your training data optimisation efforts.