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Erik on Product Management and such






    The limitations of language as a user interface


    In the evolution of human-computer interaction, each paradigm shift has broadened the scope of what users can achieve. From the command-line interfaces of the 1960s to the graphical user interfaces of the 1980s, and now, the emerging natural language interfaces (NLIs), each step has been about simplifying interaction and empowering users. However, while language-powered interfaces offer exciting possibilities, effectively using them to build better products requires understanding both their strengths and their limitations. Harnessing these interfaces correctly can enable users to accomplish their goals with less effort, but only when applied thoughtfully and with purpose.


    The Appeal and Challenges of Language as an Interface
    Natural language interfaces, driven by large language models (LLMs) like OpenAI's GPT, represent a major step forward in system interaction. These models enable users to engage in tasks they might have struggled with before, such as querying databases in conversational tones, generating creative content, or manipulating complex datasets. They excel at understanding context and generating outputs that feel natural and human-like.

    Yet, this power comes with challenges. When users are faced with a blank chat box that allows them to "say anything," they often fail to articulate their needs effectively. Writing useful prompts requires skill and experience—something most users lack. This complexity, coupled with the unpredictable nature of LLMs, can make them difficult to use effectively.

      

    The Risks of Generating Content Based on User Input
    One of the most pressing risks of using LLMs to generate content directly from user input is the potential for harmful or inappropriate outputs. LLMs, trained on vast and diverse datasets, can inadvertently reflect biases or produce results that harm a brand’s reputation or even violate laws.

    Real-World Cases:
    • OpenAI's GPT Models: In 2020, GPT-3 demonstrated how it could generate biased, offensive, or misleading outputs based on user prompts. This highlighted the challenge of ensuring the safety and appropriateness of LLM outputs in open-ended interactions.
    • Google’s Bard Launch: In 2023, Google’s AI chatbot Bard made headlines after providing incorrect information about space exploration during its public unveiling. This high-profile mistake caused reputational damage and financial consequences for Google, illustrating the risks of releasing unchecked generative systems.

    These examples underline two critical risks:
    1. Brand Damage: Generating offensive, biased, or false outputs can alienate users and undermine trust.
    2. Legal Risks: Models may produce outputs that infringe on copyrights, spread misinformation, or violate laws.

    The Role and Limitations of Guardrails
    To mitigate these risks, organizations implement guardrails—mechanisms designed to limit LLMs’ behavior and outputs. Guardrails include:
    • Filtering sensitive or inappropriate content.
    • Restricting the types of questions or tasks the model can address.
    • Hardcoding certain behaviors or error messages to prevent risky responses.

    While these safeguards reduce the likelihood of harmful outputs, they also limit the versatility of the system. Overly strict guardrails may frustrate users by rejecting valid requests or delivering unhelpful, constrained outputs. This trade-off between safety and usefulness is a core challenge in building effective products powered by LLMs.

    A Better Approach: Using LLMs to Serve Relevant Content
    Given the risks of generating freeform text, the most effective use of LLMs is not to produce outputs directly but to leverage their strengths as tools for understanding user intent and surfacing relevant, verified content. Instead of focusing on generative outputs, LLMs can act as powerful backend technologies that enhance user experiences by aligning system behavior with user goals.

    Here’s how this approach works:
    1. Understanding User Intent: Instead of generating a complete answer, LLMs can analyze user inputs to determine their goals. For instance, if a user asks, “What are some good gluten-free recipes?” the system can infer the user is seeking recipe recommendations with specific dietary constraints.
    2. Finding Relationships in Data: LLMs, paired with vector databases, can identify semantic relationships within a company’s structured and unstructured datasets. Even if the phrase “gluten-free” doesn’t explicitly appear in some recipes, the system can locate them based on indirect relationships, offering a richer set of results than traditional search engines.
    3. Serving Verified Content: Rather than presenting generated text, the system can deliver curated, high-quality results from existing databases or verified sources. For example, it could return a list of tested recipes, articles, or user reviews, ensuring reliability and trustworthiness.

    This hybrid approach leverages the strengths of LLMs—contextual understanding and pattern recognition—while avoiding the risks of unchecked content generation. It also ensures that users receive content that is both relevant and consistent with their goals.

    Language’s Precision Problem
    Despite their potential, natural language interfaces remain inherently imprecise compared to programming languages or GUIs. Natural language is ambiguous, context-dependent, and open to interpretation, which can lead to challenges such as:
    • Unclear Commands: Users may not know how to phrase their inputs for the system to understand effectively.
    • Unpredictable Outcomes: Even well-phrased prompts can yield varied and inconsistent results, frustrating users.
    • Choice Paralysis: When faced with an open-ended interface, users may feel overwhelmed and struggle to know where to begin.


    The Future of LLMs: A Backend Technology to Enhance Experiences

    The true potential of LLMs lies not in acting as standalone, user-facing chatbots but as backend technologies that enhance a product's ability to understand and respond to user needs. By processing user intent and finding meaningful relationships in data, LLMs enable products to serve curated, relevant, and trustworthy content. This approach minimizes risks while maximizing value.

    Rather than presenting users with raw, generated outputs, the system should deliver results through graphical interfaces designed to simplify interaction and build trust. By using LLMs to power the backend while relying on proven, user-friendly interfaces, we can create products that help users achieve their goals with minimal effort, clarity, and confidence.

    This vision combines the strengths of LLMs—contextual understanding and advanced search capabilities—with the reliability and predictability of structured systems, paving the way for products that are as powerful as they are user-friendly.