LLMs for marketing: Enhancing output with RAG and owned content

Large Language Models (LLMs) are an astonishing breakthrough in artificial intelligence, recognised for their human-like text generation, and ability to deliver responses fitting specific prompts made by a user.

The integration of Retrieval Augmented Generation (RAG) can enhance the capability of LLMs as owned client-facing communications channels, making them more accurate and relevant. It is also the approach used in Search Generative Experiences, and set to fundamentally change how people find and access information.

In a marketing context, RAG can enable LLMs to deliver responses aligned to an organisation’s insights, offerings, brand, and positioning by leveraging the unique knowledge contained within an organisation’s own content.

Understanding owned content

Owned content includes all the assets a company creates for its audiences, including articles, service descriptions, profiles, reports, and case studies. Within the enterprise, internal documents like technical training manuals and company reports are also owned content.

This content is frequently tailored to a brand voice and designed for relevant audiences, providing a rich source of detailed knowledge. Depending on the size of the organisation, this content is typically updated and added regularly.

Using RAG for owned LLMs

RAG can play a crucial role in enhancing the functionality of an LLM by using a controlled content source to generate responses. This ensures that the outputs are not only easily readable and well structured, based on a more broadly trained LLM, but also accurately reflect the latest offerings, opinions, and messaging an organisation wants its audience to receive.

This is particularly important in marketing, where maintaining brand consistency and up-to-date information is paramount in building trust and helping buyer audiences through their complex decision-making processes.

Potential benefits of using owned content in RAG

Key advantages of RAG using owned content:

Access to the latest data: RAG can reach the most recent content published by an organisation. This ensures LLM responses are up-to-date with the latest information, including new insights, service/product information, and brand positioning.

Specialised knowledge and expertise: Owned content is often densely packed with industry-specific information, technical details, and insights, providing a knowledge base that enhances the LLM's ability to generate accurate and relevant responses.

Authenticity and brand voice: By using content written in a brand voice, LLMs can maintain a consistent tone in their outputs, which is crucial for distinctive marketing and external communications.

Reduced bias and misinformation: Since owned content is typically more controlled and intentional than openly accessible content on the internet, it minimises the risk of external biases and misinformation, leading to more reliable outputs.

Customisation for specific industries and audiences: LLMs can be put to work in a specific context. For example, a law firm can develop an LLM that understands legal issues and regulations, thereby generating more relevant advice for its clients.

Data privacy and security: Using internal content sources minimises the risks associated with external data sourcing, ensuring compliance with privacy and security regulations.

Streamlined training process: RAG’s use of specific owned data sources reduces the need for massive generalised training data, simplifying maintenance, enhancing LLM training efficiency, and reducing cost.

Challenges and ethical considerations

Challenges that will need to be considered when utilizing RAG:

Potential internal bias and narrow perspectives: Owned content may limit the diversity of responses, introducing biases toward the organisation's viewpoint and workforce demographics.

Copyright and intellectual property issues: Ensuring all content is used within legal and ethical boundaries is crucial. This is an evolving area and one that needs close attention.

Content quality and relevance: Regular updates and reviews are needed to maintain accuracy and relevance of the content. Content hoarding must be avoided to maintain relevancy.

Privacy concerns: Addressing privacy issues in RAG-enhanced LLMs will involve anonymizing any sensitive data from internal documents, and implementing rigorous data governance practices to maintain confidentiality and trust.

Overfitting to internal jargon: There's a risk of LLMs becoming overly specialised or using internal terminology, reducing their effectiveness in general contexts.

Content silos in matrixed organisations: The challenge of aggregating and centralising content across various departments for comprehensive RAG use may be extensive and political, requiring buy-in from a broad set of content owners.

Preparing content and systems for RAG

Considerations to effectively leverage owned content for RAG:

Content curation, selection, and classification: Identifying content that aligns with the LLM's strategic objectives will enable its output to representative and diverse. Comprehensive tagging of content will enable the creating of content segments.

Content management systems: Moving to a headless CMS approach will enable far greater cross-functional alignment and reuse of content, dramatically improving efficiency and consistency.

Dynamic content assembly: Content fragments within a headless CMS can ensure that via RAG LLMs have access to the most up-to-date and relevant pieces of information including product/service descriptions down to a component level, titles, and contact information.

Data cleaning and formatting: Within content fragments, standardising content formats, styling, and other content structures for optimal compatibility with LLMs.

Anonymisation and privacy compliance: Ensuring removal of any sensitive information to adhere to privacy standards and regulations.

Quality and relevance assessment: Conducting thorough ongoing content audits to ensure the content's accuracy and current relevance is essential.

Developing a content-aware culture: Encouraging regular updates and the retirement of outdated materials to keep the LLM's data source relevant.

Integrating RAG with owned content in LLMs can significantly enhance their effectiveness in client-facing marketing scenarios, such as corporate websites. This integration allows LLMs to deliver responses that are not only accurate and up-to-date, but also closely aligned with an organisation's brand and expertise.

While RAG offers numerous benefits like specialised knowledge delivery, brand consistency, and improved data privacy, it also requires careful navigation of challenges such as potential biases, data quality, and privacy concerns.

Successful implementation and ongoing use hinges on strategic content management, utilizing advanced systems like headless CMS, and building an organisational culture of continuous content maintenance.

This is my own opinion not that of my employer.

Please do comment if you agree / disagree / have more thoughts.