Query Decomposition and Reasoning Techniques 🧠

My previous articles explored the fundamentals of Retrieval-Augmented Generation (RAG) and advanced techniques like Parallel Query Fan-Out and Reciprocal Rank Fusion. Today, we'll dive into powerful strategies that address a common RAG challenge: handling complex, multi-faceted queries that require reasoning over multiple pieces of information. 🔍

The Challenge of Complex Queries 🤔

Basic RAG systems excel at answering straightforward, factual questions, but often struggle with queries that:

1. Require multi-step reasoning 🪜

2. Combine multiple sub-questions 🧩

3. Need both retrieval and inference 🔄

4. Involve implicit information 🧿

To address these challenges, we'll explore query decomposition and reasoning techniques, which break down complex questions into manageable sub-queries and apply structured reasoning to synthesize a comprehensive answer.

Query Decomposition 🧩

Query decomposition involves breaking down a complex query into simpler sub-queries, retrieving information for each, and then combining the results to formulate a complete answer.

Implementing Basic Query Decomposition with LangChain 💻

pythonfrom langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.retrievers import VectorStoreRetriever
from typing import List

# Initialize components (using setup from previous articles)
llm = ChatOpenAI(temperature=0)
embeddings = OpenAIEmbeddings()
vectorstore = Chroma(embedding_function=embeddings, persist_directory="./chroma_db")
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})

# 1. Create a prompt for query decomposition
decompose_prompt = PromptTemplate(
    input_variables=["question"],
    template="""You need to decompose the following complex question into 2-5 simpler sub-questions. 
    These sub-questions should:
    1. Be simpler and more focused than the original question
    2. When answered together, help answer the original question
    3. Be answerable independently

    Original Question: {question}

    Sub-questions (provide as a numbered list):
    """
)

# 2. Create a chain for query decomposition
decompose_chain = LLMChain(llm=llm, prompt=decompose_prompt)

# 3. Function to decompose query and retrieve information
def decompose_and_retrieve(question: str):
    # Get sub-questions
    decomposition_result = decompose_chain.run(question=question)

    # Parse sub-questions (simple approach)
    sub_questions = []
    for line in decomposition_result.strip().split('\n'):
        if line.strip() and any(line.strip().startswith(prefix) for prefix in ["1.", "2.", "3.", "4.", "5."]):
            sub_questions.append(line.strip()[2:].strip())

    # Retrieve information for each sub-question
    sub_answers = []
    for i, sub_q in enumerate(sub_questions):
        print(f"Sub-question {i+1}: {sub_q}")
        docs = retriever.get_relevant_documents(sub_q)

        # Extract relevant information
        context = "\n\n".join([doc.page_content for doc in docs])

        # Answer sub-question
        sub_answer_prompt = PromptTemplate(
            input_variables=["context", "question"],
            template="""Use the following context to answer the question concisely:

            Context:
            {context}

            Question: {question}

            Answer:"""
        )

        sub_answer_chain = LLMChain(llm=llm, prompt=sub_answer_prompt)
        sub_answer = sub_answer_chain.run(context=context, question=sub_q)
        sub_answers.append({"question": sub_q, "answer": sub_answer, "context": context})

    return sub_questions, sub_answers

# 4. Function to synthesize the final answer
def synthesize_answer(original_question: str, sub_questions: List[str], sub_answers: List[dict]):
    # Create a synthesis prompt
    synthesis_prompt = PromptTemplate(
        input_variables=["original_question", "qa_pairs"],
        template="""You need to answer the original question based on the answers to sub-questions.

        Original Question: {original_question}

        Sub-questions and Answers:
        {qa_pairs}

        Provide a comprehensive answer to the original question, synthesizing the information from the sub-questions.

        Final Answer:"""
    )

    # Format QA pairs
    qa_text = ""
    for i, qa in enumerate(sub_answers):
        qa_text += f"Sub-question {i+1}: {qa['question']}\n"
        qa_text += f"Answer: {qa['answer']}\n\n"

    # Generate final answer
    synthesis_chain = LLMChain(llm=llm, prompt=synthesis_prompt)
    final_answer = synthesis_chain.run(original_question=original_question, qa_pairs=qa_text)

    return final_answer

# 5. Put it all together
def answer_complex_query(question: str):
    sub_questions, sub_answers = decompose_and_retrieve(question)
    final_answer = synthesize_answer(question, sub_questions, sub_answers)
    return final_answer, sub_questions, sub_answers

# Example usage
complex_query = "How did advancements in transformer architecture affect both machine translation and sentiment analysis from 2018 to 2022?"
final_answer, sub_questions, sub_answers = answer_complex_query(complex_query)
print(f"Original Question: {complex_query}")
print(f"Final Answer: {final_answer}")

Step-Back Prompting 🔍➡️🌐

Step-back prompting is a technique where the system takes a conceptual "step back" from a specific question to consider a more abstract or general perspective before addressing the original query.

Image credit: “Step-Back Prompting Enables Reasoning Via Abstraction in Large Language Models,” by Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen, Heng-Tze Cheng, Ed H.Chi QuocVLe, and Denny Zhou.

🔍 What Is Step-Back Prompting?

Step-Back Prompting involves a two-step process:​

1. Abstraction: The model is first prompted to consider a broader, more general question related to the original query. This step aims to identify overarching principles or concepts that underpin the specific problem.​

2. Reasoning: The model addresses the original, more detailed question using the insights gained from the abstraction step.

This approach mirrors human problem-solving strategies, where stepping back to understand the bigger picture can lead to more effective solutions.​

🧪 Why It Matters

Traditional prompting methods, like Chain-of-Thought, guide models through step-by-step reasoning. Step-Back Prompting adds an initial layer of abstraction, helping models to:​

• Avoid common reasoning pitfalls.

• Better handle tasks with intricate details.

• Improve accuracy in multi-step problem-solving.

This technique is particularly beneficial for tasks where understanding underlying principles is crucial.

🔁 Example of Step-Back Prompting

Task: "If you drop a metal ball and a feather from the same height in a vacuum, which will hit the ground first?"

🧠 Step-Back Prompt:

“What general principle determines how objects fall in a vacuum?”

LLM's Abstraction Response:

“In a vacuum, there is no air resistance, so all objects fall at the same rate regardless of their mass, according to Galileo’s principle of uniform acceleration.”

Final Answer Prompt:

“Given that, if you drop a metal ball and a feather from the same height in a vacuum, which will hit the ground first?”

LLM's Final Answer:

“They will hit the ground at the same time.”

🔍 Why it works: The model first reasons at an abstract, conceptual level before applying that understanding to a specific problem.

Implementing Step-Back Prompting in RAG 🚀

pythonfrom langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

# 1. Create a step-back prompt
step_back_prompt = PromptTemplate(
    input_variables=["question"],
    template="""Given a specific question, take a step back and identify the more general concept or domain that this question falls under.
    Then, formulate a more abstract question that would help establish the broader context needed to answer the specific question.

    Specific Question: {question}

    More Abstract Question:"""
)

# 2. Create a step-back chain
step_back_chain = LLMChain(llm=llm, prompt=step_back_prompt)

# 3. Enhance the RAG pipeline with step-back reasoning
def step_back_rag(question: str):
    # Generate a more abstract question
    abstract_question = step_back_chain.run(question=question)
    print(f"Original question: {question}")
    print(f"Abstract question: {abstract_question}")

    # Retrieve information for the abstract question
    abstract_docs = retriever.get_relevant_documents(abstract_question)
    abstract_context = "\n\n".join([doc.page_content for doc in abstract_docs])

    # Retrieve information for the original question
    specific_docs = retriever.get_relevant_documents(question)
    specific_context = "\n\n".join([doc.page_content for doc in specific_docs])

    # Combine contexts with preference to specific information
    combined_context = specific_context + "\n\nAdditional background information:\n" + abstract_context

    # Create a prompt for the final answer
    answer_prompt = PromptTemplate(
        input_variables=["abstract_question", "specific_question", "context"],
        template="""You are answering a specific question using both specific information and general background knowledge.

        Abstract Question: {abstract_question}
        Specific Question: {specific_question}

        Use the following context to answer the specific question:
        {context}

        First, briefly address the abstract question to establish context.
        Then, provide a detailed answer to the specific question.

        Answer:"""
    )

    # Generate the final answer
    answer_chain = LLMChain(llm=llm, prompt=answer_prompt)
    answer = answer_chain.run(
        abstract_question=abstract_question,
        specific_question=question,
        context=combined_context
    )

    return answer, abstract_question, combined_context

# Example usage
specific_query = "What were the key innovations in the BERT model that improved natural language understanding?"
answer, abstract_question, context = step_back_rag(specific_query)
print(f"Answer: {answer}")

Chain-of-Thought (CoT) Reasoning for RAG ⛓️💭

Chain-of-Thought reasoning involves breaking down the problem-solving process into explicit steps, which is particularly effective for complex reasoning tasks.

Image credit: “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models” by Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E. H., Le, Q. V., & Zhou, D.

🧠 What Is Chain-of-Thought Prompting?

CoT Prompting involves providing LLMs with examples that include a sequence of intermediate steps leading to the solution of a problem. This approach mirrors human problem-solving strategies, where breaking down complex problems into smaller, manageable steps can lead to more accurate solutions.

🔍 Why It Matters

Traditional prompting methods often fall short on tasks requiring multi-step reasoning. CoT Prompting addresses this limitation by:​

• Allowing models to decompose problems into intermediate steps.

• Providing interpretable reasoning paths aids in understanding model decisions.

• Improving performance without the need for additional training or fine-tuning.

This technique is particularly beneficial for tasks where understanding underlying principles is crucial.

🔗 Example of Chain-of-Thought Prompting

Task: "Tom has 3 times as many apples as Sarah. Together they have 48 apples. How many apples does Sarah have?"

🧠 CoT Prompt:

“Tom has 3 times as many apples as Sarah. Let’s call the number of apples Sarah has 'x'.
That means Tom has 3x apples.
Together, they have x + 3x = 4x apples.
4x = 48
Solving for x, we get x = 12.”

Final Answer:

“Sarah has 12 apples.”

🔍 Why it works: The model is guided to break the problem into small, logical steps, mimicking how humans do math word problems.

Implementing CoT in RAG Systems 🔄

pythonfrom langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

# 1. Create a CoT prompt with few-shot examples
cot_prompt = PromptTemplate(
    input_variables=["context", "question"],
    template="""You're answering a question based on the following context. Think step by step.

Context:
{context}

Question: {question}

Let's solve this step-by-step:
1. First, I'll identify the key elements in the question.
2. Then, I'll locate relevant information in the context.
3. Next, I'll analyze how the information relates to the question.
4. Finally, I'll formulate a complete answer based on this analysis.

Step 1: Identifying key elements in the question.
"""
)

# 2. Create a CoT chain
cot_chain = LLMChain(llm=llm, prompt=cot_prompt)

# 3. Function for CoT-based RAG
def cot_rag(question: str):
    # Retrieve relevant documents
    docs = retriever.get_relevant_documents(question)
    context = "\n\n".join([doc.page_content for doc in docs])

    # Generate reasoning steps and answer
    reasoning = cot_chain.run(context=context, question=question)

    # Extract final answer from reasoning (simplified approach)
    # In a real system, you might use a more sophisticated extraction method
    lines = reasoning.strip().split('\n')
    answer = ""
    for i, line in enumerate(lines):
        if "final answer" in line.lower() or "therefore" in line.lower() or "in conclusion" in line.lower():
            answer = '\n'.join(lines[i:])
            break
    if not answer:
        answer = lines[-1] if lines else reasoning

    return answer, reasoning, context

# Example usage
reasoning_query = "Based on the known limitations of transformer models, why might they struggle with extremely long documents?"
answer, reasoning, context = cot_rag(reasoning_query)
print(f"Reasoning: {reasoning}")
print(f"Answer: {answer}")

Few-Shot Prompting with Abstract and Concrete Examples 🎯📚

Few-shot prompting provides examples to guide the model's responses, which can be particularly effective when mixing abstract and concrete examples.

pythonfrom langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

# 1. Create a few-shot prompt with mixed examples
few_shot_prompt = PromptTemplate(
    input_variables=["context", "question"],
    template="""Answer the question based on the provided context. Use the examples below as a guide.

Context:
{context}

Question: {question}

Examples:

Example 1 (Abstract): 
Context: Information about various machine learning algorithms and their principles.
Question: What are the fundamental differences between supervised and unsupervised learning?
Thinking: I need to compare two major categories of machine learning. First, I'll identify the key characteristics of supervised learning. Then, I'll identify the key characteristics of unsupervised learning. Finally, I'll highlight the fundamental differences.
Answer: Supervised learning uses labeled data to train models that map inputs to known outputs, making it suitable for classification and regression. Unsupervised learning works with unlabeled data to find patterns or structure, commonly used for clustering and dimensionality reduction. The fundamental difference is that supervised learning requires labeled training data and clear target outcomes, while unsupervised learning discovers hidden patterns without predefined targets.

Example 2 (Concrete): 
Context: The BERT model was introduced in the paper "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" by Devlin et al. in 2018. It uses a transformer architecture with bidirectional self-attention and was pre-trained on tasks including masked language modeling and next sentence prediction.
Question: How does BERT's masked language modeling work?
Thinking: I need to explain a specific technique in BERT. First, I'll identify what masked language modeling is from the context. Then, I'll explain the process step by step, focusing on how tokens are masked and how the model learns to predict them.
Answer: In BERT's masked language modeling (MLM), approximately 15% of input tokens are randomly masked during pre-training. The model then learns to predict these masked tokens based on the surrounding context from both directions. This bidirectional context understanding is what distinguishes BERT from previous models that processed text in only one direction. The masking process helps BERT learn contextual representations of words that capture their meaning in different sentences.

Now answer the question step by step:
"""
)

# 2. Create a few-shot chain
few_shot_chain = LLMChain(llm=llm, prompt=few_shot_prompt)

# 3. Function for few-shot based RAG
def few_shot_rag(question: str):
    # Retrieve relevant documents
    docs = retriever.get_relevant_documents(question)
    context = "\n\n".join([doc.page_content for doc in docs])

    # Generate answer with few-shot guidance
    answer = few_shot_chain.run(context=context, question=question)

    return answer, context

# Example usage
few_shot_query = "How does the GPT model's approach to attention differ from BERT?"
answer, context = few_shot_rag(few_shot_query)
print(f"Answer: {answer}")

Advanced Implementation: Combining Techniques 🔄🔗

Let's create a comprehensive implementation that integrates query decomposition, step-back prompting, and chain-of-thought reasoning:

pythonfrom langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from typing import List, Dict

# Initialize components
llm = ChatOpenAI(temperature=0.2)
embeddings = OpenAIEmbeddings()
vectorstore = Chroma(embedding_function=embeddings, persist_directory="./chroma_db")
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})

class AdvancedRagReasoner:
    def __init__(self, retriever, llm):
        self.retriever = retriever
        self.llm = llm

        # Initialize all prompts and chains
        self._init_prompts()
        self._init_chains()

    def _init_prompts(self):
        # Query decomposition prompt
        self.decompose_prompt = PromptTemplate(
            input_variables=["question"],
            template="""Decompose the following complex question into 2-4 simpler sub-questions that would help answer the original question when combined.

            Original Question: {question}

            Sub-questions (provide as a numbered list):"""
        )

        # Step-back prompt
        self.step_back_prompt = PromptTemplate(
            input_variables=["question"],
            template="""Given a specific question, identify the more general concept or domain this question belongs to.
            Then, formulate a higher-level question that would provide helpful background knowledge.

            Specific Question: {question}

            More General Question:"""
        )

        # Sub-question answering prompt
        self.sub_answer_prompt = PromptTemplate(
            input_variables=["context", "question", "general_context"],
            template="""Use the following information to answer the question step by step.

            Primary Context:
            {context}

            Background Information:
            {general_context}

            Question: {question}

            Think through this step by step:"""
        )

        # Synthesis prompt
        self.synthesis_prompt = PromptTemplate(
            input_variables=["original_question", "sub_qa_pairs", "general_answer"],
            template="""Synthesize a comprehensive answer to the original question based on the sub-question answers and general background.

            Original Question: {original_question}

            General Background:
            {general_answer}

            Relevant Sub-questions and Answers:
            {sub_qa_pairs}

            Provide a coherent answer that addresses the original question, synthesizing all relevant information:"""
        )

    def _init_chains(self):
        self.decompose_chain = LLMChain(llm=self.llm, prompt=self.decompose_prompt)
        self.step_back_chain = LLMChain(llm=self.llm, prompt=self.step_back_prompt)
        self.sub_answer_chain = LLMChain(llm=self.llm, prompt=self.sub_answer_prompt)
        self.synthesis_chain = LLMChain(llm=self.llm, prompt=self.synthesis_prompt)

    def decompose_query(self, question: str) -> List[str]:
        """Decompose a complex query into sub-questions"""
        decomposition_result = self.decompose_chain.run(question=question)

        # Parse sub-questions
        sub_questions = []
        for line in decomposition_result.strip().split('\n'):
            if line.strip() and any(line.strip().startswith(prefix) for prefix in ["1.", "2.", "3.", "4.", "5."]):
                sub_questions.append(line.strip()[2:].strip())

        return sub_questions

    def get_general_context(self, question: str) -> Dict:
        """Apply step-back reasoning to get general context"""
        general_question = self.step_back_chain.run(question=question)
        general_docs = self.retriever.get_relevant_documents(general_question)
        general_context = "\n\n".join([doc.page_content for doc in general_docs])

        # Get a concise answer to the general question
        general_answer_prompt = PromptTemplate(
            input_variables=["context", "question"],
            template="""Provide a concise overview answering this general question:

            Context:
            {context}

            General Question: {question}

            Overview:"""
        )

        general_answer_chain = LLMChain(llm=self.llm, prompt=general_answer_prompt)
        general_answer = general_answer_chain.run(
            context=general_context,
            question=general_question
        )

        return {
            "question": general_question,
            "context": general_context,
            "answer": general_answer
        }

    def answer_sub_questions(self, sub_questions: List[str], general_context: str) -> List[Dict]:
        """Answer each sub-question using chain-of-thought reasoning"""
        sub_answers = []

        for sub_q in sub_questions:
            # Retrieve specific information for this sub-question
            docs = self.retriever.get_relevant_documents(sub_q)
            context = "\n\n".join([doc.page_content for doc in docs])

            # Generate reasoned answer
            answer = self.sub_answer_chain.run(
                context=context,
                question=sub_q,
                general_context=general_context
            )

            sub_answers.append({
                "question": sub_q,
                "context": context,
                "answer": answer
            })

        return sub_answers

    def synthesize_final_answer(self, original_question: str, sub_answers: List[Dict], general_info: Dict) -> str:
        """Synthesize the final answer from all components"""
        # Format sub-QA pairs
        qa_text = ""
        for i, qa in enumerate(sub_answers):
            qa_text += f"Sub-question {i+1}: {qa['question']}\n"
            qa_text += f"Answer: {qa['answer']}\n\n"

        # Generate final answer
        final_answer = self.synthesis_chain.run(
            original_question=original_question,
            sub_qa_pairs=qa_text,
            general_answer=general_info["answer"]
        )

        return final_answer

    def answer_complex_query(self, question: str) -> Dict:
        """Main method to answer a complex query using all strategies"""
        # Step 1: Get general context through step-back reasoning
        general_info = self.get_general_context(question)
        print(f"General question: {general_info['question']}")

        # Step 2: Decompose the query
        sub_questions = self.decompose_query(question)
        print(f"Sub-questions: {sub_questions}")

        # Step 3: Answer each sub-question
        sub_answers = self.answer_sub_questions(sub_questions, general_info["context"])

        # Step 4: Synthesize final answer
        final_answer = self.synthesize_final_answer(question, sub_answers, general_info)

        return {
            "original_question": question,
            "general_question": general_info["question"],
            "general_answer": general_info["answer"],
            "sub_questions": sub_questions,
            "sub_answers": sub_answers,
            "final_answer": final_answer
        }

# Example usage
reasoner = AdvancedRagReasoner(retriever, llm)
complex_query = "How have transformer architecture innovations impacted both machine translation quality and computational efficiency over the past five years?"
result = reasoner.answer_complex_query(complex_query)
print(f"\nFinal Answer: {result['final_answer']}")

When to Use Different Reasoning Techniques 🧠⚙️

Each reasoning technique has specific strengths and ideal use cases:

Query Decomposition 🧩

Best for:

• Multi-part questions requiring information from different knowledge domains

• Questions that combine multiple concepts or periods

• Comparative analyses that involve multiple distinct elements

• Complex queries that would exceed context window limitations if handled directly

Example use cases:

• "Compare the impact of transformer models on NLP and computer vision applications" 📊

• "How did COVID-19 affect global supply chains and what strategies emerged to address these challenges?" 🦠🔄

• "What are the similarities and differences between IBM's, Google's, and Microsoft's quantum computing approaches?" 💻🔄

Step-Back Prompting 🔍➡️🌐

Best for:

• Questions requiring a broader context to understand fully

• Topics where specific details make sense only in a larger framework

• Questions where the user might be missing important background knowledge

• Queries that benefit from establishing first principles

Example use cases:

• "Why doesn't BERT perform well on document-level reasoning tasks?" (step back to transformer architecture limitations) 🤔

• "How should I implement attention mechanisms in my model?" (step back to attention mechanism principles) 🧩

• "What makes GPT different from other language models?" (step back to transformer architecture evolution) 📈

Chain-of-Thought Reasoning ⛓️💭

Best for:

• Questions requiring logical reasoning or multi-step inference

• Mathematical or algorithmic problems

• Cause-and-effect analysis

• Questions where the reasoning process is as important as the answer

Example use cases:

• "Based on the information, what might be causing the model's hallucinations?" 🔍

• "How would changing the learning rate affect training in this scenario?" 📉

• "What would the computational complexity impact of doubling the attention heads be?" 📊

Few-Shot Prompting 🎯📚

Best for:

• Specialized domains with unique reasoning patterns

• Situations where you want consistent answer formats

• Cases where abstract principles and concrete examples both matter

• When you need to guide the model to a specific type of response

Example use cases:

• Scientific literature analysis 🧪

• Technical documentation generation 📝

• Comparative evaluations requiring standard criteria ⚖️

• Pattern recognition in specialized domains 🔄

Why This Matters: The Importance of Advanced RAG Reasoning ✨🚀

Advanced reasoning techniques address fundamental limitations in basic RAG systems:

1. Handling complexity: Real-world questions rarely fit into simple retrieval patterns. Decomposition and reasoned synthesis allow handling of intricate, multi-faceted queries. 🧩

2. Bridging knowledge gaps: Step-back prompting helps connect specific details to general principles, providing necessary context even when not explicitly requested. 🌉

3. Transparency and explainability: Chain-of-thought reasoning makes the system's logic visible, increasing user trust and enabling debugging. 🔍

4. Accuracy improvement: By structuring the reasoning process, these techniques reduce hallucinations and improve the overall quality of responses. 📈

5. Knowledge synthesis: Moving beyond mere retrieval to true synthesis of information addresses one of the biggest limitations of traditional RAG systems. 🧪

These advanced techniques transform RAG from a simple retrieval-then-generate system into an intelligent reasoning engine capable of tackling complex queries with the sophistication of expert human researchers. 🧠✨

Conclusion 🏁

As RAG systems evolve from basic information retrieval tools to sophisticated reasoning engines, query decomposition, step-back prompting, and chain-of-thought reasoning become essential. These approaches enable RAG systems to handle complex, multi-faceted queries requiring information retrieval, valid reasoning, and synthesis.

By implementing the strategies outlined in this article, developers can create RAG systems that:

• Break down complex questions into manageable components 🧩

• Establish proper context through step-back reasoning 🔍

• Apply structured thinking through chain-of-thought ⛓️

• Leverage the power of examples through few-shot prompting 🎯

The result is a RAG system that doesn't just retrieve information but reasons with it, delivering responses that demonstrate true understanding rather than simple pattern matching. As we continue pushing the boundaries of AI reasoning, these techniques will become foundational in building knowledgeable knowledge systems. 🚀🔮

References

Zheng, H. S., Mishra, S., Chen, X., Cheng, H.-T., Chi, E. H., Le, Q. V., & Zhou, D. (2023). Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models. arXiv:2310.06117.​

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E. H., Le, Q. V., & Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv:2201.11903.