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Building Trustworthy AI: A Multi-Pronged Approach to LLM Steering

Olga Miroshnyk
Olga Miroshnyk
Jun 18, 2024
3 min read


"Accuracy is king" was the initial mantra when models like GPT-3 emerged, captivating us with their ability to generate human-like text with impressive precision. However, as the initial excitement waned, a crucial realization dawned: accuracy alone wasn't enough. Much like a sculptor needs more than just a chisel to create a masterpiece, LLMs require a more nuanced approach – one that goes beyond mere accuracy. This is where the concept of LLM steering comes into play. It's about guiding LLMs beyond simply producing factually correct outputs, but also ensuring their outputs are reliable, transparent, and aligned with user intent. This article delves into the various techniques and approaches that make up this LLM steering process, ultimately aiming to create LLMs that are not just impressive feats of engineering, but trustworthy tools that seamlessly integrate with human values.


Shift to a Broader Perspective: Beyond Accuracy

The initial focus on accuracy as the sole metric for LLMs was a natural starting point. After all, generating factually correct outputs seemed paramount for tasks like information retrieval or basic language translation. However, as LLMs were deployed in increasingly sensitive environments, the limitations of this narrow focus became starkly apparent.

Take, for example, the healthcare sector. Here, an LLM generating an accurate yet fabricated diagnosis could have disastrous consequences. Similarly, in legal or financial settings, even a seemingly accurate LLM output could be misleading without proper context or explanation. This potential for harm highlighted the need for a broader set of priorities beyond just accuracy.

Furthermore, the inherent opacity of LLM decision-making processes raised significant concerns about transparency and accountability. Users had no way of understanding how LLMs arrived at their outputs, making it difficult to trust their recommendations or identify potential biases. This lack of transparency hampered responsible AI development and limited the potential applications of LLMs.

Therefore, a paradigm shift began to take hold. The focus moved from mere accuracy to a more comprehensive set of criteria, including:

Avoiding hallucinations: This involves implementing safeguards to prevent LLMs from generating factually incorrect or misleading information.

Providing explainable outputs: This means enabling users to understand the rationale behind LLM recommendations, fostering trust and confidence.

This shift in focus was driven by a combination of practical and ethical considerations.  On the practical side, ensuring reliable and verifiable outputs was crucial for real-world applications.  From an ethical standpoint, minimizing potential harm and fostering user trust became paramount for responsible LLM development.

Steering the Course: A Multi-Pronged Approach to LLM Alignment

As we move beyond a focus solely on accuracy, a multi-pronged approach is crucial for ensuring reliable and trustworthy LLM outputs. This approach, known as LLM steering, strives to guide LLMs not just towards producing factually correct outputs, but also towards outputs that are aligned with user intent, transparent, and minimize the risk of hallucinations.  Here, we delve into the various techniques that fall under two key categories:  Guardrails and Controls and User Interaction and Context Building.

Guardrails and Controls

These techniques act as safety nets, preventing LLMs from straying into the realm of "hallucination" and ensuring factual accuracy.

Anti-Hallucination Guardrails 

These methods shield against false information by:

  • Data filtering and pre-training: Curating high-quality training data with minimal factual inconsistencies and biases lays a strong foundation for truthful outputs.
  • Real-time fact-checking: Integrating fact-checking mechanisms allows LLMs to verify information on the fly, further ensuring accuracy.
Goal-Based Flows with Dynamic Guardrails

This approach tailors LLM responses to specific user goals. By understanding the user's intent, the LLM activates context-appropriate "guardrails" that guide its generation process.  For instance, an LLM tasked with legal research might have stricter safeguards against generating fabricated legal codes compared to one composing a fictional story.

User Interaction and Context Building

Effective LLM steering relies on continuous learning and adaptation. Here's where user interaction and context building come into play:

Dynamic Data Collection from Chat

During user interaction, various data points can be gathered to refine the LLM's understanding of user intent and context:

  • Contact details (optional): With user consent, collecting contact information allows for follow-up clarification or feedback on LLM outputs, aiding in continuous improvement.
  • Discovery questions: Asking targeted questions during interaction helps the LLM refine its understanding of the user's intent and context, leading to more focused and relevant outputs.
Custom Vocabulary and Contextual Behaviors

Tailoring the LLM's communication style plays a vital role in achieving alignment.

  • Custom vocabulary: Developing domain-specific vocabularies allows LLMs to communicate more effectively within specific fields (e.g., legal, medical).
  • Contextual behaviors: By adjusting the LLM's communication style based on context (formal vs. informal), the user experience becomes more natural and engaging.

Fact-Check & Generation Validation

While not explicitly listed above, fact-checking and generation validation are crucial aspects of both categories. They ensure outputs are not only aligned but also factually accurate.

By implementing these diverse techniques, LLM steering can guide outputs beyond mere accuracy and embark on a journey of true alignment with user intent. It's an ongoing process of learning and adaptation, ultimately leading to the creation of LLMs that are not just accurate, but trustworthy and aligned with human needs.


The journey towards truly aligned and reliable LLMs is far from over. The techniques explored in this article – anti-hallucination guardrails, dynamic controls, user interaction, and contextual tailoring – represent a significant step forward, but the quest for responsible AI development requires ongoing exploration and collaboration.

Here are some exciting trends shaping the future of LLM steering and human-AI collaboration:  

  • Explainable AI (XAI): Developing methods for LLMs to explain their reasoning and decision-making processes will further enhance transparency and trust.
  • Human-in-the-Loop Learning: Integrating human feedback and oversight into the LLM training process can help mitigate biases and ensure alignment with human values.
  • Continuous Benchmarking and Evaluation: Regularly evaluating LLM performance across various metrics, including factual accuracy, explainability, and user satisfaction, is crucial for ongoing improvement.

By fostering a collaborative environment where researchers, developers, and users work together, we can unlock the full potential of LLMs. As we move forward, the focus will not just be on impressive feats of engineering, but on creating trustworthy AI tools that seamlessly integrate with human values and empower us to solve complex challenges. This collaborative effort paves the way for a future where LLMs are not just tools, but responsible partners in our endeavors.

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