Enhancing LLM performance with reasoning using deterministic feedback loops

Large Language Models (LLMs) have revolutionized how we interact with AI, offering unparalleled capabilities in natural language understanding and generation. However, their design—centered on predicting text based on statistical patterns—presents significant challenges for tasks requiring deep reasoning, logical consistency, and grounded decision-making. LLMs often produce shallow responses, are constrained by single-pass computation, and lack grounding in verifiable facts. To address these limitations, an innovative solution has emerged: reasoning with feedback loops, powered by deterministic systems like KBAI (Knowledge-Based AI).

In this post, we explore how reasoning with feedback loops, managed by KBAI, enhances LLM performance by breaking tasks into manageable parts and processing them step-by-step with an external logical loop. We’ll see a practical example of analyzing employment contracts and draw parallels with Google’s AlphaEvolve, which uses similar feedback-driven reasoning to optimize algorithms. This approach ensures accuracy, reliability, and transparency while directly tackling the compute limitations of LLMs.

The compute limitations of LLMs in reasoning tasks

LLMs are optimized for predicting the next token based on previous ones, using a fixed amount of compute per token. This means that for each token generated, the model performs a constant amount of computation, regardless of the task’s complexity. Unlike humans or specialized reasoning systems, which can allocate more time and effort to complex problems, LLMs are constrained to a single forward pass per token, without the ability to backtrack or refine their reasoning within that pass. This leads to several critical limitations:

• Shallow Reasoning: LLMs often rely on surface-level patterns, missing deeper logical connections required for complex tasks like mathematical proofs or legal analysis.
• Inability to Handle Multi-Step Problems: For tasks requiring multiple reasoning steps, LLMs can make errors in intermediate steps that propagate to the final answer, with no built-in mechanism to correct them.
• Lack of Dynamic Compute Allocation: LLMs cannot adjust their computational effort based on task complexity, unlike humans who can pause, reflect, and iterate on difficult problems.

For example, consider a multi-step problem like determining the validity of an employment contract based on specific criteria (e.g., presence of a non-compete clause and signatures from both parties). An LLM might misinterpret clauses or skip critical conditions in a single pass, leading to unreliable results. This makes LLMs less suitable for applications requiring precision and consistency, such as legal analysis or algorithm optimization.

(This is discussed in more detail by Andrej Karpathy in his talk Deep Dive into LLMs like ChatGPT, as well as, from a slightly different angle, by Stephen Wolfram in his article What Is ChatGPT Doing … and Why Does It Work?)

What is reasoning with feedback loops?

Reasoning with feedback loops addresses these limitations by enabling AI systems to process complex tasks incrementally through a cycle of fact extraction and rule application. Instead of expecting an LLM to solve a problem in one go, the task is divided into smaller sub-tasks. An external logical loop, managed by a deterministic system like KBAI, guides the process by determining which facts are needed and querying the LLM to extract them iteratively.

For LLMs, this means multiple interactions where KBAI, guided by its deterministic rules, iteratively queries the LLM to extract specific facts required for the reasoning process. The process works as follows:

• KBAI identifies the next fact needed based on its predefined rules.
• It queries the LLM to extract that specific fact from the available data.
• The LLM returns the fact, and KBAI uses it to update its knowledge and determine the next fact to query, if any.
• This cycle repeats until KBAI has all the facts necessary to apply its rules and reach a conclusion.
• Finally, LLM may use facts and reasoning from KBAI output to generate a natural language response based on the conclusion.

This method prevents shallow reasoning by encouraging deeper analysis, overcomes compute-bound constraints by distributing effort across iterations, and ensures grounded reasoning by anchoring decisions in explicit logic and verifiable facts.

KBAI deterministic reasoning

To tackle the compute limitations of LLMs, we can leverage a deterministic reasoning engine like KBAI. KBAI allows users to define a knowledge base of rules that guide the reasoning process. These rules are explicit and deterministic, ensuring that the same inputs always produce the same outputs, guaranteeing consistency and reliability.

In the context of reasoning with feedback loops, KBAI manages the process by:

• Identifying which facts are needed to apply the rules.
• Querying the LLM for additional information when necessary.
• Applying the rules to make decisions or draw conclusions based on gathered facts.

For example, in analyzing an employment contract, KBAI knows that to determine validity, it needs to check for a non-compete clause and signatures from both parties. If these facts are not initially available, KBAI iteratively queries the LLM to extract them from the document, using feedback to refine its approach.

This rule-based approach complements the probabilistic nature of LLMs:

• The LLM excels at understanding and generating natural language, making it ideal for extracting facts from unstructured text.
• KBAI provides the structured reasoning framework that ensures the final decision is grounded in logic and verifiable facts.

By combining these strengths, we can overcome the compute limitations of LLMs and achieve more accurate and reliable results.

How reasoning with feedback loops works

The key to overcoming LLM compute limitations lies in reasoning with feedback loops. Instead of relying on a single-pass LLM output, the task is broken into smaller, manageable parts. An external logical loop, managed by KBAI, handles the iteration, passing each part to the LLM and refining the approach based on feedback. Here’s a step-by-step breakdown:

1. Initialization: The external system (KBAI) starts with the initial task or query.
2. Fact Gathering: KBAI determines which facts are needed to proceed and queries the LLM to extract those facts from the available data (e.g., a document).
3. Rule Application: Using the gathered facts, KBAI applies predefined rules to make decisions or draw conclusions.
4. Feedback and Iteration: If more facts are needed or if the rules require further steps, KBAI provides feedback to guide the next query to the LLM, repeating the process until the task is complete.

This approach distributes computational effort across multiple iterations, allowing for deeper reasoning and more accurate results. It’s like having a supervisor who guides the LLM through the problem, ensuring each step is correctly handled before moving to the next.

Case study: analyzing employment contract validity

Let’s explore a practical application: using reasoning with feedback loops to determine if an employment contract is valid. The criteria are straightforward: the contract must have a non-compete clause and be signed by both parties. Here’s how KBAI and an LLM work together in a feedback-driven loop.

The Feedback Loop process

1. Initial Task: The user provides the contract text, and KBAI is tasked with determining its validity based on predefined rules.
2. Fact Gathering: KBAI checks its rule—“If the contract has a non-compete clause and is signed by both parties, then it is valid”—but lacks the facts to proceed. It identifies two required facts:
   • Does the contract include a non-compete clause?
   • Is the contract signed by both parties?
3. Query the LLM: KBAI queries the LLM: “Does the contract include a non-compete clause?” The LLM analyzes the text and responds, “Yes.”
4. Feedback and Update: KBAI updates its knowledge base with this fact and checks if all necessary facts are gathered. Since it still needs to know if the contract is signed by both parties, it queries the LLM again: “Is the contract signed by both parties?” The LLM confirms, “Yes.”
5. Apply the Rule: With all facts gathered—hasNonCompeteClause: true and isSignedByBothParties: true—KBAI applies the rule and concludes that the contract is valid.
6. Present the Result: The system explains the decision: “The contract is valid because it includes a non-compete clause and is signed by both parties.”

Why it works

This feedback loop ensures:

• No Shallow Reasoning: Each condition is explicitly checked, avoiding assumptions based on surface-level patterns.
• Overcoming Compute Limits: The task is split across multiple lightweight calls, bypassing the fixed compute per token constraint.
• Grounded Results: Decisions are anchored in verified facts and deterministic rules, not statistical guesses.

By using KBAI to manage the feedback loop, the reasoning process is deterministic, ensuring consistency and reliability, while leveraging the LLM’s strength in natural language understanding.

Insights from Google’s AlphaEvolve

The concept of reasoning with feedback loops is not unique to KBAI but is also exemplified in Google DeepMind’s AlphaEvolve, unveiled in May 2025. AlphaEvolve is an evolutionary coding agent that combines LLMs like Gemini with automated evaluators to discover and optimize algorithms. It uses fast and reliable feedback loops to iteratively refine solutions, as described by Google DeepMind:

“This allows us to establish fast and reliable feedback loops to improve the system.” (Google DeepMind Blog)

How AlphaEvolve Uses Feedback Loops

• Process: AlphaEvolve generates code variants using LLMs, evaluates them using automated metrics (e.g., correctness, efficiency), and selects the best variants for further refinement.
• Applications: It has optimized algorithms for data center efficiency, chip design, AI training processes, and mathematical problems like matrix multiplication.
• Example: For a mathematical problem, AlphaEvolve iteratively generated code, evaluated its performance, and refined it until it outperformed existing solutions, as documented in a Google Colab notebook (GitHub - google-deepmind/alphaevolve_results).

This feedback-driven approach mirrors the KBAI process, where external evaluation ensures that each iteration improves the solution, overcoming the single-pass compute limitations of LLMs.

Benefits of reasoning with feedback loops

This approach offers clear advantages over relying solely on LLMs:

• Accuracy: Step-by-step verification catches errors a single-pass LLM might miss.
• Consistency: Deterministic rules ensure the same inputs always yield the same outputs.
• Transparency: Explicit rules and feedback make the reasoning traceable and explainable.
• Scalability: Breaking tasks into parts makes complex problems manageable.
• Compute Efficiency: Distributing effort across iterations bypasses the fixed compute per token constraint, allowing for deeper reasoning without requiring massive computational resources.

As noted in the context of KBAI, this method can reduce debugging time by up to 30%, enabling faster deployment of reliable AI solutions.

Broader applications

Reasoning with feedback loops is versatile and can enhance LLM performance across various domains:

• Compliance Checks: Verifying regulatory adherence in documents by iteratively extracting and evaluating facts.
• Financial Analysis: Evaluating data against predefined criteria through iterative fact-checking.
• Code Debugging: Iteratively identifying and fixing errors in code by evaluating outputs against expected results.
• Algorithm Optimization: Similar to AlphaEvolve, using feedback loops to refine and improve algorithms.
• Legal Document Analysis: Systematically checking contracts or legal texts against legal standards.

By pairing LLMs with deterministic tools like KBAI or systems like AlphaEvolve, industries can build trustworthy AI systems for critical tasks.

Conclusion

Reasoning with feedback loops, powered by deterministic systems like KBAI or evolutionary agents like AlphaEvolve, unlocks the potential of LLMs by addressing their core weaknesses—shallow reasoning, compute constraints, and lack of grounding. By breaking tasks into smaller steps and managing the process with an external feedback loop, this approach transforms LLMs into reliable partners for complex decision-making. Whether analyzing contracts or optimizing algorithms, this method ensures AI is accurate, consistent, and transparent—qualities essential for real-world impact.