In 2024, the PGA Tour tackled AI-generated content accuracy issues by implementing Retrieval-Augmented Generation (RAG), integrating a 190-page rulebook to provide precise, real-time golf statistics. Meanwhile, Bayer leveraged fine-tuning, training AI models on proprietary agricultural data to enhance domain-specific insights. These real-world applications highlight the ongoing debate of RAG vs Fine Tuning.
While RAG offers adaptability by fetching up-to-date information during inference, fine-tuning embeds domain expertise directly into the model. Both methods have distinct advantages, but how do you decide which one is right for your needs? In this blog , we’ll break down the key differences between RAG vs Fine Tuning, exploring their strengths, limitations, and ideal use cases
What is Retrieval-Augmented Generation (RAG)? Retrieval-Augmented Generation (RAG) is an advanced AI framework that improves text generation by incorporating external information retrieval. Instead of relying solely on a model’s pre-trained knowledge, RAG dynamically fetches relevant data from an external source (such as a database, document collection, or the web) before generating a response.
How RAG Works Query Processing : A user inputs a query. Retrieval : The model searches for relevant information from an external knowledge base using a retrieval system (e.g., vector search, semantic search, BM25). Augmentation : The retrieved data is provided as an additional context to the model. Generation : The model generates a response based on both the retrieved information and its pre-trained knowledge. Kanerika’s RAG-Based LLM Chatbot: DokGPT What is DokGPT? DokGPT is a RAG-based LLM chatbot that allows users to interact with enterprise data, document repositories, and collections of files. It retrieves precise and relevant information without manual searching, ensuring efficiency and accuracy across various business applications.
Core Functionalities Customizable Data Modularity – Users can adjust retrieval settings to either prioritize accuracy (fewer but highly relevant results) or increase data volume (broader information with possible redundancies). Multilingual Query & Response – DokGPT can process queries in one language while retrieving answers from documents in another. Cross-Document Consolidation – Instead of returning separate results, DokGPT merges relevant information from multiple documents into a single, well-structured response, improving readability and context retention. Advanced Media & Data HandlingKey Business Use Cases Enterprise Knowledge Base: Enables quick access to project documents, policies, and operational insights. HR & Employee Support: Provides instant responses to onboarding, policy, and process-related queries. Customer Support Automation: Assists users by providing real-time product guidance, troubleshooting, and FAQs.
What is Fine-Tuning? Fine-tuning is especially valuable when dealing with specialized tasks that require a deeper understanding of context beyond what a general model can provide. By training on domain-specific data, the model learns to recognize patterns, terminology, and nuances that are crucial for accurate responses.
By tailoring a model to industry-specific jargon, customer interactions, or unique problem-solving scenarios, fine-tuning ensures that AI systems provide more relevant, reliable, and context-aware responses.
How Fine-Tuning Works Start with a Pre-trained Model : Use a large model (like GPT, BERT, or ResNet) that has already been trained on massive datasets. Select a Domain-Specific Dataset : Gather a smaller dataset relevant to the target task (e.g., legal documents for a legal AI assistant ). Adjust Model Weights : Train the model on the new dataset, updating its weights while retaining prior knowledge. Optimize & Validate : Fine-tune hyperparameters and evaluate performance on validation data to prevent overfitting. Key Differences: RAG Vs Fine Tuning Feature Retrieval-Augmented Generation (RAG) Fine-Tuning Approach Retrieves external data before generating a response Trains a model further on a specific dataset Data Source Uses an external knowledge base or document store Uses labeled training data specific to the task Flexibility Dynamic; adapts to new data without retraining Static; requires retraining for updates Accuracy Improves factual correctness by retrieving fresh data Enhances model understanding of a domain Computational Cost Lower, as it avoids full retraining Higher, due to additional training steps Best For Tasks requiring up-to-date or broad knowledge (e.g., chatbots, research tools) Tasks needing deep understanding of specialized data (e.g., legal AI, medical diagnosis) Example Use Cases Real-time Q&A, search-augmented assistants Domain-specific AI models, customer support bots Updates Easily updated by modifying external knowledge sources Requires new training when data changes
RAG Vs Fine Tuning: A Detailed Comparison 1. Model Adaptability RAG (Retrieval-Augmented Generation)
Flexibility: The model does not need retraining when new information becomes available. It dynamically retrieves relevant data at inference time. Continuous Learning: By integrating an updated knowledge base, RAG ensures that responses stay relevant without modifying the model itself. Example: A legal AI assistant that references the latest legal documents and case laws without requiring frequent retraining.
Fine-Tuning
Static Knowledge: Once fine-tuned, the model remains fixed until it is explicitly retrained with new data. Requires Periodic Updates: To stay relevant, the model must be retrained whenever new domain knowledge is introduced. Example: A medical AI model that has been fine-tuned on past medical journals but requires updates when new treatments or diseases emerge. Transform Your Business with AI-Powered Solutions! Partner with Kanerika for Expert AI implementation Services
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2. Handling Domain-Specific Knowledge RAG (Retrieval-Augmented Generation)
Broad Knowledge Scope: Works well with general or structured knowledge but may struggle with deeply specialized topics unless retrieval is optimized. External Data Access: Can fetch domain-specific information but requires a well-maintained external knowledge source. Example: A tech support chatbot retrieving real-time hardware troubleshooting steps from an external product manual.
Fine-Tuning
Deep Specialization: Fine-tuning ensures the model inherently understands specific domain jargon and nuances. Pretrained for Accuracy: Can be customized with industry-specific datasets to improve contextual understanding. Example: A scientific research assistant AI fine-tuned on biomedical literature to understand complex genetic interactions. 3. Latency and Response Time RAG (Retrieval-Augmented Generation)
Higher Latency: Since it fetches external data in real-time, responses may take longer, especially if retrieval involves large datasets. Dependency on Retrieval System: The speed of response depends on how quickly the system can fetch relevant information. Example: A news summarization AI retrieving and summarizing the latest headlines from various sources before generating a response.
Fine-Tuning
Lower Latency: The model generates responses instantly since all knowledge is embedded within its parameters. No External Calls: Since fine-tuning stores all learned information within the model, there’s no need to fetch data externally, making responses faster. Example: A financial trading assistant that instantly generates stock market insights based on pre-trained historical data. Discover how advanced Retrieval-Augmented Generation (RAG) can enhance AI-driven data retrieval for more accurate, efficient, and context-aware results
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4. Interpretability and Transparency RAG (Retrieval-Augmented Generation)
Clear Citation of Sources: Can display the original sources of retrieved information, increasing trust and interpretability. Easy Fact Verification: Users can verify data since RAG retrieves content from known databases or documents. Example: A research assistant AI providing article summaries along with source links for easy verification.
Fine-Tuning
Opaque Decision-Making: The model’s outputs are based on stored knowledge without showing where the information originated. Difficult to Trace Errors: If the model generates incorrect responses, it’s harder to determine where the mistake came from. 5. Maintenance and Updates RAG (Retrieval-Augmented Generation)
Minimal Model Updates: Since knowledge is external, updates only require modifying the knowledge base rather than retraining the model. Less Downtime: Businesses can continuously update information without taking the model offline. Example: A customer service AI that pulls information from an updated FAQ database, ensuring users always get the latest answers.
Fine-Tuning
Frequent Retraining Required: Every time new information is added, the entire model needs retraining, which can be costly and time-consuming. Risk of Outdated Responses: If not updated frequently, the model may provide obsolete information. Example: A medical diagnosis AI that needs retraining every year with new clinical research findings to stay accurate. 6. Use in Regulated or Sensitive Environments RAG (Retrieval-Augmented Generation)
Preferred for Auditable Fields: Suitable for industries where it is crucial to trace information back to its source (e.g., healthcare, law, finance). Dynamic Compliance Management: Can integrate with compliance databases to ensure regulatory adherence in real time. Example: A healthcare chatbot retrieving medical guidelines from an official database to provide accurate, up-to-date advice.
Fine-Tuning
Better for Controlled Environments: Works well when information should not change frequently or when direct AI-generated responses are required. More Secure but Less Transparent: Since fine-tuning doesn’t rely on external sources, it may reduce security risks but lacks traceability. Example: A banking AI fine-tuned to generate financial reports based on pre-approved company policies, reducing external data dependencies. When to Choose RAG (Retrieval-Augmented Generation) Retrieval-Augmented Generation (RAG) is a powerful approach for enhancing the capabilities of large language models (LLMs) by integrating external knowledge sources. Here are the key scenarios where RAG is the ideal choice:
1. Need for Up-to-Date Information RAG excels in environments where real-time or frequently updated data is critical. By connecting the model to live databases, APIs, or web sources, it ensures access to the latest information. This makes it particularly useful for applications like:
2. Handling Knowledge-Intensive Tasks If your application requires detailed factual knowledge or domain-specific expertise that is not included in the model’s training data, RAG is a better option. It dynamically retrieves relevant documents or data from external sources, making it suitable for:
Knowledge-intensive content generation Technical or academic writing 3. Avoiding Model Fine-Tuning RAG does not require modifying the underlying model, which reduces complexity and resource requirements. This makes it ideal when:
You lack the computational resources or expertise for fine-tuning. The task involves diverse or rapidly changing datasets that would make fine-tuning impractical. Multimodal RAG: Everything You Need to Know Learn how Multimodal RAG enhances AI by integrating text, images, and videos for more accurate and context-aware information retrieval
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4. Ensuring Transparency and Interpretability RAG allows users to trace the sources of retrieved information, enhancing trust and accountability in generated outputs. This is particularly important in applications like:
Legal or financial advisory systems Medical decision support tools 5. Versatility Across Applications RAG supports a wide range of use cases by combining generative and retrieval-based techniques. It balances creativity with factual accuracy, making it suitable for:
Educational platforms requiring accurate and context-aware responses. 6. Fast-Changing Environments In industries where knowledge evolves rapidly, such as technology or healthcare, RAG ensures adaptability by continuously incorporating new information without retraining the model.
By leveraging external knowledge dynamically, RAG offers a scalable and flexible solution for applications that demand accuracy, relevance, and up-to-date responses.
When to Choose Fine-Tuning Fine-tuning a large language model (LLM) involves customizing a pre-trained model for a specific task or domain by training it further on specialized datasets. Below are the key scenarios where fine-tuning is the optimal choice:
1. Task or Domain-Specific Requirements Fine-tuning is ideal when your application requires the model to handle highly specialized tasks or domains. By training the model on domain-specific data, it can better understand unique terminology, language patterns, and contextual nuances. Examples include:
Medical diagnosis support Industry-specific chatbots. 2. High Accuracy and Performance Needs When precision is critical, fine-tuning enhances the model’s ability to generate accurate and contextually relevant outputs. This is particularly beneficial for:
3. Proprietary or Confidential Data If your application relies on proprietary or sensitive data that is not publicly available, fine-tuning allows you to incorporate this data into the model securely. This ensures better alignment with your organization’s unique knowledge base while maintaining privacy .
4. Frequent Use of a Specific Task For tasks that are repeated often within a business or workflow, fine-tuning can significantly improve efficiency and consistency. For instance:
Automated customer support responses Product recommendation systems. 5. Custom Style or Output Requirements Fine-tuning is effective when you need the model to generate outputs in a specific tone, style, or format. This is useful for applications such as:
Creative writing tools (e.g., generating poetry or scripts) Tailored marketing content creation. 6. Limited Generalization Needs If your use case does not require broad generalization across diverse topics but instead focuses on a narrow scope, fine-tuning ensures the model performs optimally within that scope.
Compare leading LLMs to identify the ideal solution that aligns with your business needs and goals.
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Combining RAG and Fine-Tuning In many scenarios, combining Retrieval-Augmented Generation (RAG) and fine-tuning can yield superior results by leveraging the strengths of both approaches. This hybrid strategy is particularly useful when addressing complex or dynamic use cases. Below are the key methods and benefits of combining these techniques:
1. Retrieval-Augmented Fine-Tuning (RAFT) RAFT involves using RAG to retrieve relevant data and then fine-tuning the model on this curated dataset. This approach enhances the model’s ability to generate accurate and contextually relevant outputs while tailoring it to specific tasks or domains1 4 . Example: A legal AI assistant could retrieve case law using RAG and then be fine-tuned on this data for improved accuracy in legal reasoning. 2. Fine-Tuning RAG Components Instead of fine-tuning the entire model, specific components of a RAG system, such as the retriever or generator, can be fine-tuned to address performance gaps. This targeted fine-tuning improves the system’s ability to retrieve and process domain-specific information effectively. 3. Balancing Dynamic and Static Knowledge RAG is ideal for accessing dynamic, real-time information, while fine-tuning enables specialization in static or domain-specific tasks. Combining both ensures that the model remains adaptable to new data while excelling in specialized tasks. 4. Improved Scalability and Accuracy By integrating RAG’s retrieval capabilities with fine-tuned domain expertise, organizations can scale their AI systems without sacrificing accuracy. This hybrid approach is particularly valuable for applications requiring both up-to-date knowledge and deep contextual understanding, such as customer support or research tools Kanerika is a fast-growing tech services company specializing in AI and data-driven solutions that help businesses overcome challenges, enhance operations, and drive measurable results. We design and deploy custom AI models tailored to specific business needs, boosting productivity, efficiency, and cost optimization.
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FAQs Is RAG the same as fine-tuning? RAG and fine-tuning are fundamentally different approaches to enhancing large language models. Retrieval-augmented generation dynamically fetches external information at inference time without modifying the model’s weights. Fine-tuning permanently adjusts model parameters through additional training on domain-specific datasets. RAG excels when you need real-time, up-to-date responses while fine-tuning works best for embedding specialized knowledge or behavior patterns. Each serves distinct use cases, and understanding the RAG vs fine-tuning distinction helps enterprises select the right optimization strategy. Kanerika’s AI specialists can evaluate your requirements and recommend the optimal approach for your business goals.
Is RAG cheaper than fine-tuning? RAG typically costs less than fine-tuning for most enterprise deployments. Fine-tuning requires significant computational resources for training, specialized GPU infrastructure, and ongoing retraining whenever knowledge updates occur. RAG eliminates retraining costs by simply updating your knowledge base or vector database. However, RAG introduces ongoing retrieval infrastructure expenses and latency considerations during inference. For organizations needing frequently updated information, retrieval-augmented generation delivers better cost efficiency. Fine-tuning proves more economical only when model behavior must change permanently. Kanerika helps enterprises calculate true cost comparisons between RAG and fine-tuning based on your specific workloads.
Why might a business choose fine-tuning instead of RAG? Businesses choose fine-tuning over RAG when they need the model to internalize specific behaviors, tone, or specialized reasoning patterns. Fine-tuning excels for proprietary terminology adoption, consistent output formatting, or domain-specific tasks where knowledge rarely changes. Industries like legal, medical, or technical manufacturing benefit when model responses require embedded expertise rather than retrieved documents. Fine-tuning also reduces inference latency since no external retrieval step occurs. When your use case demands predictable, style-consistent outputs without external dependencies, fine-tuning delivers superior results. Kanerika’s LLM experts help enterprises determine when fine-tuning aligns with your operational requirements.
Is LLM better than RAG? LLMs and RAG serve complementary purposes rather than competing alternatives. A base LLM generates responses from its trained knowledge, while RAG enhances that same LLM by providing external context during inference. RAG-augmented systems outperform standalone LLMs for knowledge-intensive tasks requiring current or proprietary information. Without RAG, LLMs rely solely on training data that becomes outdated. The real comparison involves evaluating whether your use case benefits from retrieval-augmented generation or operates sufficiently with fine-tuned base models. Kanerika designs intelligent AI architectures that combine LLM capabilities with RAG to maximize response accuracy for enterprise applications.
Is fine-tuning better than RAG? Neither fine-tuning nor RAG is universally superior; effectiveness depends on your specific use case. Fine-tuning excels when you need consistent model behavior, specialized language patterns, or task-specific performance without external dependencies. RAG outperforms fine-tuning for knowledge-heavy applications requiring real-time information access or frequent content updates. Fine-tuning embeds knowledge permanently while RAG retrieves it dynamically. For rapidly evolving data environments, retrieval-augmented generation proves more practical. For stable, behavior-focused applications, fine-tuning delivers better results. Kanerika evaluates your enterprise requirements to architect the optimal RAG, fine-tuning, or hybrid solution for your AI initiatives.
What is the difference between RAG, fine-tuning, and prompt engineering? RAG, fine-tuning, and prompt engineering represent three distinct LLM customization strategies. Prompt engineering crafts input instructions to guide model responses without any modifications. Fine-tuning adjusts model weights through additional training on custom datasets, permanently altering behavior. RAG retrieves external documents at inference time to augment responses with current, relevant context. Prompt engineering requires no infrastructure changes while fine-tuning demands computational resources. RAG needs vector databases and retrieval pipelines. Each approach addresses different requirements, and enterprises often combine all three for optimal results. Kanerika implements comprehensive LLM optimization strategies tailored to your specific business objectives and technical infrastructure.
Can RAG and fine-tuning be used together? RAG and fine-tuning work exceptionally well together in hybrid architectures. Fine-tuning customizes the model’s reasoning patterns, output style, and domain-specific language comprehension. RAG then supplements this fine-tuned model with real-time external knowledge during inference. This combination delivers both behavioral consistency and access to current information. For example, a fine-tuned legal LLM with RAG can maintain proper legal terminology while retrieving the latest case law. Hybrid approaches often outperform either technique alone for enterprise applications requiring both specialized behavior and dynamic knowledge access. Kanerika architects combined RAG and fine-tuning solutions that maximize your AI investment returns.
Is RAG better than fine-tuning for hallucinations? RAG significantly reduces hallucinations compared to fine-tuning alone for knowledge-based queries. Retrieval-augmented generation grounds responses in retrieved documents, providing verifiable sources the model references during generation. Fine-tuning cannot guarantee factual accuracy since models may still generate plausible but incorrect information from learned patterns. RAG enables citation of sources, making fact-checking straightforward. However, RAG effectiveness depends on retrieval quality; poor document retrieval still produces unreliable outputs. For mission-critical accuracy requirements, RAG combined with source verification delivers the most trustworthy results. Kanerika implements RAG pipelines with robust retrieval mechanisms to minimize hallucination risks in your AI applications.
When should I use RAG instead of fine-tuning? Choose RAG over fine-tuning when your knowledge base changes frequently, when you need source attribution, or when working with large document collections. RAG excels for customer support systems requiring current product information, research applications needing recent publications, or compliance scenarios demanding traceable answers. If your data updates weekly or monthly, retraining fine-tuned models becomes impractical. RAG also suits situations where enterprise data cannot be embedded in model weights due to security concerns. When real-time accuracy matters more than customized model behavior, retrieval-augmented generation delivers superior outcomes. Kanerika assesses your use case to determine whether RAG implementation best serves your enterprise AI goals.
What is RAG and why is it used? RAG, or retrieval-augmented generation, combines information retrieval with language model generation to produce more accurate, contextual responses. The system first searches a knowledge base or vector database for relevant documents, then passes these as context to the LLM for response generation. Enterprises use RAG to ground AI outputs in verified information, reduce hallucinations, and enable access to proprietary or current data not present in model training. RAG eliminates costly retraining cycles while maintaining response quality for knowledge-intensive applications. It transforms static LLMs into dynamic systems capable of leveraging organizational knowledge assets. Kanerika builds enterprise RAG solutions that unlock value from your existing data repositories.
What are the main challenges of RAG? RAG implementation faces several technical challenges that impact performance. Retrieval quality determines response accuracy; irrelevant document retrieval produces poor outputs. Chunking strategies must balance context preservation with embedding efficiency. Latency increases since every query requires retrieval before generation. Vector database maintenance demands ongoing attention as knowledge bases grow. Enterprise RAG systems must handle document versioning, access controls, and multi-format ingestion. Semantic search limitations can miss relevant content when queries differ linguistically from stored documents. Despite these challenges, properly architected RAG pipelines deliver substantial accuracy improvements over standalone LLMs. Kanerika’s data engineers overcome these RAG challenges through proven implementation methodologies.
What is the biggest advantage of fine-tuning? Fine-tuning’s greatest advantage is permanently embedding specialized behavior and domain expertise directly into model weights. Once fine-tuned, the model consistently produces outputs matching your required style, terminology, and reasoning patterns without external dependencies. Inference remains fast since no retrieval step occurs. Fine-tuning enables models to excel at specific tasks like sentiment classification, entity extraction, or format-specific generation. The model internalizes patterns impossible to achieve through prompting alone. For applications requiring consistent, predictable outputs aligned with organizational standards, fine-tuning delivers unmatched reliability. Kanerika’s AI team executes fine-tuning projects that transform base models into purpose-built enterprise assets.
Does fine-tuning improve accuracy? Fine-tuning improves accuracy for specific tasks when properly executed with quality training data. It enhances performance on domain-specific language, specialized terminology recognition, and consistent output formatting. However, fine-tuning does not guarantee factual accuracy; models can still generate incorrect information confidently. Accuracy improvements depend heavily on training data quality, quantity, and relevance to target use cases. For knowledge-based accuracy, RAG typically outperforms fine-tuning by grounding responses in retrieved facts. Fine-tuning excels at improving task accuracy, style consistency, and reasoning patterns rather than factual correctness. Kanerika validates fine-tuning effectiveness through rigorous evaluation frameworks before production deployment.
When not to use fine-tuning? Avoid fine-tuning when your knowledge base changes frequently, as retraining costs become prohibitive. Skip fine-tuning if you lack sufficient high-quality training data; poor data produces unreliable models. When you need source attribution or audit trails, fine-tuning cannot provide citation capabilities that RAG delivers. Avoid it for general knowledge queries where base models already perform adequately. Fine-tuning also fails when you need responses reflecting real-time information since training data becomes stale immediately. For rapidly evolving domains or when computational resources are limited, RAG or prompt engineering offer more practical alternatives. Kanerika helps enterprises identify whether fine-tuning fits their requirements or if alternative approaches better serve their needs.
Is fine-tuning still relevant? Fine-tuning remains highly relevant despite RAG’s growing popularity. It serves irreplaceable functions for embedding specialized behaviors, consistent output formatting, and domain-specific language patterns that retrieval cannot address. Many enterprise applications require both approaches. Fine-tuning creates efficient, specialized models for production workloads where response speed matters. For edge deployment scenarios with limited connectivity, fine-tuned models operate independently without retrieval infrastructure. As foundation models improve, fine-tuning increasingly focuses on behavior customization rather than knowledge injection. The technique continues evolving through methods like LoRA and QLoRA that reduce computational requirements. Kanerika implements modern fine-tuning approaches that deliver customized AI capabilities efficiently.
Is RAG considered training? RAG is not considered training because it does not modify model weights or parameters. Training and fine-tuning adjust the neural network’s internal representations through gradient-based optimization on datasets. RAG operates entirely at inference time, retrieving external context that the unchanged model uses for generation. This distinction matters significantly for deployment: RAG requires no GPU-intensive training cycles, model versioning complexities, or risk of catastrophic forgetting. However, building RAG systems requires training embedding models and optimizing retrieval pipelines. The core language model remains frozen throughout RAG implementation. Kanerika deploys RAG solutions that enhance your existing LLM investments without disruptive retraining requirements.
Can LLM still hallucinate even with RAG? LLMs can still hallucinate even with RAG implementation, though the frequency decreases substantially. Hallucinations persist when retrieval returns irrelevant documents, when the model ignores retrieved context in favor of parametric knowledge, or when queries fall outside the knowledge base coverage. Poor chunking strategies may provide incomplete context that leads to fabricated completions. Models occasionally synthesize information incorrectly even from accurate sources. Effective RAG systems implement relevance scoring thresholds, citation requirements, and confidence indicators to flag potential hallucinations. Retrieval quality directly correlates with hallucination reduction. Kanerika builds RAG architectures with robust guardrails that minimize hallucination risks for enterprise-critical applications.
Does RAG require more computational resources than fine-tuning? RAG and fine-tuning have different computational profiles across training and inference phases. Fine-tuning requires substantial GPU resources during the training phase but operates efficiently at inference. RAG requires minimal initial compute since no training occurs, but adds retrieval overhead during every inference request. Production RAG systems need vector database infrastructure, embedding generation compute, and retrieval latency management. For high-throughput applications, RAG’s per-request retrieval costs accumulate significantly. Fine-tuning front-loads computational investment while RAG distributes costs over time. Total resource requirements depend on query volume, knowledge base size, and update frequency. Kanerika analyzes your computational constraints to architect cost-effective RAG or fine-tuning solutions.
