RAG vs Fine-Tuning: Which Approach Should You Choose?

Large language models can be customized in two fundamentally different ways: by providing external knowledge at inference time or by changing the model’s behavior through additional training. These approaches are known as Retrieval-Augmented Generation (RAG) and LLM fine-tuning, and they solve different problems despite often being discussed as competing alternatives.

This guide compares RAG vs fine tuning across the factors that matter most in production AI systems, including accuracy, knowledge freshness, implementation complexity, cost, latency, maintenance effort, and enterprise use cases. Rather than recommending one technique universally, the article provides a practical decision framework to help engineering teams determine whether they need external knowledge retrieval, behavioral customization, or a hybrid architecture combining both approaches.

What Are RAG and Fine-Tuning?

Retrieval-Augmented Generation (RAG) enhances a language model by supplying relevant external information during inference. Instead of relying solely on the model’s internal knowledge, a RAG system retrieves documents from an external knowledge base, selects the most relevant passages, and includes them in the prompt before generation. The underlying model remains unchanged, while its responses become grounded in current enterprise data.

LLM fine-tuning, by contrast, modifies the model itself through additional supervised training. Rather than retrieving information dynamically, fine-tuning teaches the model new behaviors, terminology, writing styles, or task-specific patterns by updating its learned parameters. The objective is not to provide new documents during inference, but to permanently adjust how the model responds.

Although these techniques are often compared directly, they address different architectural challenges and are frequently combined within modern enterprise AI systems.

How Retrieval-Augmented Generation Works

A typical RAG pipeline begins by ingesting documents into an external knowledge base. The content is split into smaller passages, converted into vector embeddings using an embedding model, and stored inside a vector database together with metadata and access permissions.

When a user submits a request, the system performs semantic search to retrieve the most relevant passages. These retrieved documents are added to the prompt as contextual information before the language model generates its answer. Modern RAG systems often include reranking, metadata filtering, citation generation, and permission-aware retrieval to improve both relevance and security.

Because knowledge remains outside the model, documents can be updated without retraining the underlying LLM.

How LLM Fine-Tuning Works

Fine-tuning adapts a pre-trained language model by training it on additional labeled examples that reflect the desired behavior. Instead of supplying external documents during inference, the training process updates the model’s internal parameters so that future responses naturally follow new patterns, formats, or domain-specific conventions.

Most enterprise projects use supervised fine-tuning, where curated input-output pairs teach the model how to respond in particular situations. Depending on the size of the model and available resources, organizations may use full fine-tuning or more efficient approaches such as parameter-efficient fine-tuning (PEFT) and LoRA, which modify only a small subset of model parameters while reducing computational cost.

Fine-tuning changes the model itself rather than the information it receives.

RAG vs Fine-Tuning at a Glance

At a high level, RAG vs LLM fine tuning can be summarized as a comparison between external knowledge retrieval and behavioral adaptation. RAG focuses on keeping information current by retrieving documents during inference, while fine-tuning specializes the model’s behavior through additional training.

Neither approach replaces the other. RAG is generally preferred when answers depend on frequently changing information, private enterprise documentation, or source citations. Fine-tuning is more appropriate when organizations need consistent formatting, specialized terminology, structured outputs, or repeatable task-specific behavior.

For many enterprise AI applications, the most effective architecture combines both techniques, using retrieval to provide accurate information while relying on fine-tuning to improve reasoning style, response consistency, and domain adaptation.

Key Differences Between RAG and Fine-Tuning

The fundamental difference between RAG vs fine tuning lies in what each technique changes. RAG modifies the information available to the model during inference, while fine-tuning modifies the model itself.

With Retrieval-Augmented Generation, the language model remains unchanged and retrieves relevant information from an external knowledge base whenever a request is received. Fine-tuning, however, permanently adjusts the model’s behavior through additional training using carefully prepared datasets.

This distinction influences nearly every engineering decision, including implementation effort, infrastructure, maintenance, update frequency, explainability, privacy, and long-term operational cost. Understanding whether your primary challenge is missing knowledge or inconsistent model behavior is usually the fastest way to choose the appropriate solution.

What Changes: External Context vs Model Weights

The most important architectural distinction is what each approach modifies.

RAG leaves the model weights untouched. Instead, it retrieves relevant documents from an external knowledge base and injects them into the prompt as contextual information. As the knowledge base evolves, the model immediately benefits from updated information without requiring retraining.

Fine-tuning changes the model’s learned parameters through supervised training. New behavior becomes part of the model itself, allowing it to consistently follow specialized instructions, terminology, formatting rules, or domain-specific conventions. Updating this knowledge, however, generally requires another training cycle.

Knowledge Freshness and Update Speed

When business information changes frequently, retrieval augmented generation usually provides a significant operational advantage. Updating a RAG system typically involves adding or modifying documents in the knowledge base, rebuilding embeddings if necessary, and refreshing the search index. New information can often become available to the model within minutes.

Fine-tuning follows a much slower update cycle because new knowledge must first be collected, cleaned, labeled, incorporated into a training dataset, validated, and deployed as a new model version. As a result, fine-tuning is better suited to relatively stable knowledge and long-term behavioral adaptation than continuously changing content.

Data Requirements and Preparation

The two approaches require fundamentally different data assets.

A RAG system depends on high-quality source documents, metadata, document chunking strategies, embedding generation, indexing, and retrieval evaluation. Success depends primarily on the quality and organization of the underlying knowledge repository rather than labeled training examples.

Fine-tuning requires carefully curated training datasets consisting of representative input-output pairs. These examples should demonstrate the desired behavior consistently and accurately across many scenarios. Preparing such datasets often involves data cleaning, deduplication, annotation, quality review, and leakage prevention before training begins.

For many organizations, preparing reliable training data requires substantially more effort than building an initial retrieval pipeline.

Accuracy, Groundedness, and Hallucination Risk

Both approaches can improve model quality, but they do so in different ways.

RAG increases accuracy and groundedness by allowing the model to generate answers based on retrieved evidence rather than relying solely on its internal knowledge. Source citations and document retrieval also make responses easier to verify. However, poor retrieval quality or incomplete documents can still produce incorrect or partially grounded answers.

Fine-tuning improves consistency, domain adaptation, and response style by teaching the model preferred behaviors. It can reduce certain classes of errors for repetitive tasks but does not automatically provide access to new factual information. Unless combined with retrieval, a fine-tuned model may still hallucinate when asked about information that was never included during training.

RAG vs Fine-Tuning Comparison

Implementation Time and Technical Complexity

From an engineering perspective, RAG and LLM fine-tuning require very different implementation efforts. A production-ready RAG solution involves building a complete retrieval layer, including document ingestion, chunking, embedding generation, vector indexing, semantic search, reranking, access control, citation handling, and continuous synchronization with enterprise data sources.

Fine-tuning shifts the complexity toward machine learning operations. Teams must prepare high-quality training datasets, define evaluation criteria, run supervised training experiments, validate new model versions, perform safety testing, and establish deployment and rollback procedures.

Neither approach is inherently simpler. RAG introduces additional infrastructure but avoids modifying the model, whereas fine-tuning requires a mature ML pipeline and ongoing model lifecycle management.

Training, Inference, and Maintenance Costs

Cost considerations extend far beyond the initial implementation. Fine-tuning requires investment in dataset preparation, compute resources for training, experiment tracking, model storage, version management, and periodic retraining as business requirements evolve.

RAG avoids model training but introduces operational costs associated with embedding generation, vector databases, retrieval infrastructure, indexing pipelines, and additional inference steps. Depending on retrieval strategy and document volume, these components may increase latency and infrastructure costs.

Organizations should evaluate the total cost of ownership rather than focusing only on development expenses. Long-term maintenance, update frequency, infrastructure operations, monitoring, and engineering effort often have a greater financial impact than the initial implementation.

Explainability, Citations, Security, and Privacy

One of RAG’s strongest advantages is explainability. Because answers are generated from retrieved documents, the system can provide source citations, allowing users to verify where information originated. This capability is particularly valuable in regulated industries, internal knowledge assistants, and enterprise search applications where traceability is essential.

Fine-tuned models generally cannot identify the specific training examples that influenced a response. Although they may demonstrate excellent domain knowledge, explaining why a particular answer was generated is significantly more difficult.

Security considerations also differ. RAG requires careful permission-aware retrieval, document-level access controls, metadata filtering, and protection of sensitive enterprise content. Fine-tuning reduces dependence on live retrieval during inference but demands strict governance of training datasets, privacy protection, and controls against unintended memorization of confidential information.

When Should You Choose RAG?

Retrieval-Augmented Generation is usually the preferred option when the application depends on current, private, or frequently changing information. Enterprise knowledge assistants, document search, policy lookup, technical documentation, legal research, and customer support systems often benefit from retrieval because they require answers grounded in authoritative sources.

RAG is particularly effective when users expect source citations, document traceability, permission-aware access, or rapid knowledge updates without retraining the model. It also works well in organizations where documentation changes frequently or where multiple repositories must be searched during every request.

If the primary challenge is keeping information accurate and up to date, RAG is generally a more appropriate architectural choice than fine-tuning.

Dynamic or Frequently Updated Knowledge

When enterprise knowledge changes continuously, RAG offers a significant operational advantage. Product catalogs, pricing, internal policies, technical documentation, inventory information, and compliance requirements can all be updated directly within the knowledge base without modifying the language model.

This approach reduces deployment cycles because engineering teams only need to refresh documents and retrieval indexes rather than retraining and redeploying the model. As a result, RAG is particularly well suited for organizations where information evolves daily and outdated responses could create operational or compliance risks.

Enterprise Search, Support, and Document Q&A

Enterprise search is one of the most common applications of Retrieval-Augmented Generation. Employees can ask natural-language questions and receive answers synthesized from internal documentation, knowledge bases, policies, onboarding materials, product manuals, or technical specifications.

Unlike traditional keyword search, RAG combines semantic retrieval with language generation, allowing users to receive concise, contextual answers while preserving links to the original documents. Permission-aware retrieval ensures that users access only information they are authorized to view, making RAG an effective foundation for enterprise knowledge assistants, customer support systems, and document question-answering applications.

Use Cases That Require Source Citations

Some AI applications must explain where their answers originate. Industries such as healthcare, finance, legal services, insurance, and the public sector often require traceable responses that can be verified against approved documentation.

Because RAG generates answers from retrieved evidence, it naturally supports citations, document references, and verification workflows. Users can inspect the supporting materials instead of relying solely on the model’s internal knowledge. This improves trust, simplifies auditing, and reduces the risk of unsupported claims in environments where factual accuracy is critical.

When Should You Choose Fine-Tuning?

Fine-tuning is the stronger option when the main requirement is consistent model behavior rather than access to new or frequently changing facts. It is useful when a generative AI application must follow a specific tone, produce a stable output structure, apply domain terminology correctly, or perform a narrow task repeatedly with minimal variation.

A strong prompt-engineering baseline should usually come first. If carefully designed prompts and examples still produce inconsistent results, fine-tuning may improve reliability by embedding the preferred behavior into the model itself.

This approach is especially valuable for repetitive workflows where teams already have a representative, high-quality training dataset. The required cost and implementation time depend on the selected model, dataset size, labeling quality, evaluation process, and deployment infrastructure.

Consistent Style, Format, and Model Behavior

Fine-tuning can improve consistency when an application must follow precise formatting rules, maintain a recognizable brand voice, or apply specialized instructions across thousands of requests.

Typical examples include standardized customer replies, controlled report structures, domain-specific writing conventions, fixed JSON schemas, and repeatable internal communication formats. Instead of including extensive instructions and examples in every prompt, the desired behavior becomes part of the customized model.

However, training examples must reflect the target behavior accurately. Inconsistent labels, weak examples, or contradictory formatting patterns can reduce reliability rather than improve it. Teams should therefore validate a prompt-based baseline first and fine-tune only when repeated evaluations show a clear and measurable quality gap.

Task Specialization and Domain Adaptation

Fine-tuning can improve performance on a narrow, repeatable task by teaching the model domain-specific terminology, conventions, and decision patterns. This is particularly useful for specialized coding assistance, industry-specific content generation, internal command interpretation, or structured operational workflows.

For example, a company may fine-tune a model to understand proprietary product categories, internal abbreviations, or a consistent decision taxonomy. The goal is not necessarily to store large volumes of factual knowledge inside the model, but to help it behave more reliably within a clearly defined domain.

Domain adaptation works best when teams have representative examples covering both common scenarios and important edge cases. A narrow, well-defined objective is generally more suitable than trying to make one fine-tuned model handle every enterprise task.

Classification, Extraction, and Structured Outputs

Fine-tuning is often effective for repetitive tasks such as classification, information extraction, normalization, and structured output generation. Examples include assigning support tickets to predefined categories, extracting fields from recurring document types, converting natural-language requests into internal commands, or returning data in a validated schema.

A task-specific model may reduce prompt complexity and improve consistency when thousands of similar requests follow the same structure. However, performance should be evaluated against a strong prompt-only baseline because modern foundation models can already handle many structured tasks without additional training.

For production use, teams should validate field accuracy, schema compliance, class balance, edge cases, and confidence thresholds. Training duration and cost vary according to the model, data volume, provider workflow, and evaluation requirements.

RAG vs LLM Fine-Tuning by Use Case

The most practical way to compare fine tuning vs retrieval augmented generation is to evaluate the dominant requirement of the application. RAG is usually preferred when the system must answer from current, private, or traceable knowledge. Fine-tuning is more suitable when the model must behave consistently, follow specialized instructions, or perform a repetitive task in a predictable format.

Some products require both. A fine-tuned model can follow domain-specific response rules while a RAG pipeline supplies current and verifiable information during inference. This hybrid approach can improve quality, but it also adds retrieval infrastructure, model lifecycle management, evaluation complexity, and operational cost.

The best architecture therefore depends on the business workflow, data volatility, citation requirements, desired behavior, risk level, and measurable production outcomes.

Customer Support and Knowledge Assistants

For an enterprise knowledge assistant, RAG is usually the foundation because answers depend on current product documentation, policies, account information, onboarding materials, and troubleshooting guides.

A practical system retrieves approved passages from internal repositories, applies user permissions, and generates an answer with links or citations to the original sources. This makes updates easier because teams can refresh the knowledge base without retraining the model.

Fine-tuning may still help with tone, intent classification, escalation decisions, or response structure. In many support environments, the strongest architecture combines retrieval for factual knowledge with targeted behavioral customization for consistent service delivery.

Content Generation and Brand Voice

Fine-tuning may be useful when content must follow a highly consistent brand voice, terminology standard, formatting structure, or repeated editorial pattern. A task-specific content model can learn these conventions from approved examples and reproduce them more reliably than a long prompt alone.

RAG can supplement the system with current product details, campaign briefs, legal claims, or updated brand guidelines. This is particularly important when generated content must include recent information that should not be embedded permanently into model weights.

A hybrid approach often works best: fine-tuning controls behavior and style, while retrieval supplies fresh, approved facts at generation time.

Document Processing and Data Extraction

Document-heavy workflows may benefit from either approach depending on the task. Fine-tuning can improve extraction, classification, and normalization when documents follow recurring formats and the expected outputs are stable. Examples include invoices, claims, application forms, and standardized operational records.

RAG is more appropriate when users need to locate relevant sections across large document collections or ask questions about documents whose content changes frequently.

A practical solution may use retrieval to identify the correct source passages and a specialized model to extract or classify the required information. Evaluation should cover OCR quality, schema validity, document layout, confidence scoring, and exceptions requiring human review.

Domain-Specific AI Agents and Copilots

Many domain-specific copilots require both current knowledge and specialized behavior. RAG provides access to proprietary documentation, policies, customer records, or technical standards, while fine-tuning can improve terminology, output formats, action selection, and recurring decision patterns.

A legal copilot, for example, may retrieve current policies and approved precedents while using a customized model to produce a controlled review format. An engineering assistant may retrieve internal architecture documentation and apply domain-specific coding or analysis conventions.

These systems also require tool integrations, permissions, observability, and workflow evaluation. The added complexity is justified only when the hybrid architecture produces measurable improvements over a simpler prompt-based or RAG-only baseline.

Can You Combine RAG and Fine-Tuning?

Yes. In production systems, RAG and fine-tuning are complementary rather than competing approaches. RAG supplies current, verifiable knowledge during inference, while a fine-tuned model delivers specialized behavior, consistent formatting, or domain-specific reasoning. Together, they address both knowledge access and model behavior.

A hybrid architecture is most valuable when an application requires up-to-date enterprise information alongside highly predictable responses. For example, an enterprise assistant may retrieve current company policies through RAG while relying on a fine-tuned model to generate responses that follow internal writing standards, compliance requirements, or structured output formats.

The trade-off is additional architectural complexity. Organizations should adopt a hybrid approach only when measurable improvements justify the extra infrastructure, operational effort, and maintenance.

How a Hybrid RAG and Fine-Tuning Architecture Works

A hybrid architecture combines retrieval with model customization in a single inference workflow. The user request is first analyzed to determine what information is needed. Relevant documents are then retrieved from the knowledge base, ranked, filtered, and supplied as context to the fine-tuned model, which generates the final response using both external evidence and its learned behavioral patterns.

A simplified workflow looks like this:

In this architecture, retrieval is responsible for knowledge freshness, while the customized model is responsible for response quality, consistency, and task execution.

When the Hybrid Approach Is Worth the Complexity

A hybrid solution should be considered only when both current knowledge and specialized behavior are business-critical. Typical indicators include rapidly changing enterprise information, strict formatting requirements, domain-specific terminology, complex workflows, and measurable quality improvements that cannot be achieved through prompt engineering or RAG alone.

Hybrid systems are common in regulated industries, enterprise copilots, customer support platforms, legal assistants, and engineering knowledge systems. However, they also require stronger engineering capabilities because retrieval, model behavior, evaluation, and monitoring must all be validated independently before being assessed as a complete application.

If either retrieval or behavioral customization alone solves the business problem, a simpler architecture is generally preferable.

How to Choose Between RAG and Fine-Tuning: Step by Step

Choosing between RAG or fine tuning should begin with the business problem rather than the underlying technology. Engineering teams should first determine whether the application lacks current knowledge, consistent behavior, or both. From there, they can evaluate data availability, update frequency, operational constraints, and expected business outcomes.

A practical decision framework is shown below:

This process helps organizations avoid unnecessary complexity while ensuring that the selected architecture aligns with real production requirements rather than technology trends.

Define Whether You Have a Knowledge or Behavior Problem

The first decision is identifying the primary limitation of the current system.

If the model lacks access to specific, private, or constantly changing information, the problem is usually one of knowledge, making RAG the more appropriate solution. Examples include internal documentation, company policies, technical manuals, customer records, or product catalogs.

If the model already has sufficient knowledge but produces inconsistent outputs, ignores formatting rules, struggles with domain terminology, or performs a narrow task unreliably, the challenge is primarily one of behavior. In these situations, fine-tuning is often the more suitable approach.

Assess Data Volume, Quality, and Update Frequency

The available data should strongly influence the architectural decision.

RAG depends on accessible, well-structured documents with appropriate permissions, metadata, and retrieval quality. Fine-tuning depends on representative labeled examples that accurately demonstrate the desired behavior across a wide range of scenarios.

Organizations should also evaluate how often their information changes. Frequently updated content generally favors RAG, while relatively stable tasks with consistent behavioral expectations are better candidates for fine-tuning. In many enterprise environments, the availability and quality of data ultimately determine which approach is practical.

Compare Quality, Latency, Cost, and Compliance Needs

The final decision should balance technical performance with operational constraints. Teams should compare expected task success, factual accuracy, citation requirements, response latency, infrastructure complexity, maintenance effort, privacy requirements, regulatory obligations, and long-term operating costs.

Rather than relying on assumptions, organizations should evaluate both approaches using representative workloads and measurable success criteria. Implementation time, infrastructure cost, and maintenance effort vary significantly depending on the selected foundation model, retrieval architecture, dataset quality, deployment environment, and traffic volume.

The best solution is the one that delivers the required business outcomes with the lowest sustainable operational complexity.

Build a Baseline and Evaluate Both Options

Before committing to either architecture, teams should establish a strong baseline using prompt engineering and evaluate both approaches under identical production-like conditions. This prevents premature optimization and provides objective evidence for architectural decisions.

A practical evaluation should compare task success, factual accuracy, groundedness, citation quality, latency, infrastructure complexity, operational cost, maintenance effort, and business KPIs. The same representative test set should be used for each approach to ensure fair comparison.

Rather than assuming that RAG or fine-tuning will automatically produce better results, organizations should validate improvements through measurable experiments. In many projects, the simplest solution that consistently meets business requirements remains the best long-term choice.

Decision Matrix for Choosing RAG or Fine-Tuning

RAG vs Fine-Tuning Decision Checklist

Choosing the right architecture is easier when the decision is reduced to a structured checklist rather than assumptions. Before implementation, engineering teams should evaluate both business requirements and technical constraints.

Consider the following questions:

• Does the application depend on current or frequently changing information?
• Do users require verifiable source citations?
• Is the knowledge primarily stored in enterprise documents?
• Does the model already know the information but behave inconsistently?
• Are representative labeled training examples available?
• How often will content need to be updated?
• What are the acceptable latency, infrastructure, and maintenance costs?
• Are there regulatory, privacy, or compliance requirements?
• Is a hybrid architecture expected to produce measurable improvements over a simpler solution?

Answering these questions helps determine whether RAG, fine-tuning, or a hybrid architecture provides the best balance between quality, complexity, and long-term maintainability.

Common Mistakes When Customizing an LLM

Many organizations adopt the wrong customization strategy because they attempt to solve every problem with a single technique. One of the most common mistakes is using fine-tuning to memorize rapidly changing facts that should instead be retrieved from an external knowledge base. Conversely, some teams build complex RAG pipelines when the real issue is inconsistent model behavior that would be better addressed through behavioral customization.

Other frequent mistakes include using low-quality training data, skipping prompt-engineering baselines, ignoring retrieval evaluation, optimizing only for benchmark accuracy, overlooking latency and infrastructure costs, and combining RAG with fine-tuning before validating either approach independently.

Successful AI projects typically begin with the simplest architecture capable of meeting business requirements and introduce additional complexity only when objective evaluation demonstrates measurable value.

RAG and fine-tuning solve different problems and should not be viewed as competing technologies. Retrieval-Augmented Generation improves responses by supplying relevant external knowledge during inference, while LLM fine-tuning improves the model itself by teaching it new behaviors, formats, or domain-specific patterns.

For many enterprise AI applications, the most effective solution is not choosing one approach over the other but selecting the right architecture based on business requirements. Knowledge assistants, document search, and policy-driven systems typically benefit from RAG, whereas classification models, structured content generation, and specialized workflows often benefit from fine-tuning. Hybrid architectures combine the strengths of both techniques but should be adopted only when measurable quality improvements justify the additional engineering effort.

The best decision comes from objective evaluation. By comparing RAG, fine-tuning, and hybrid approaches against representative workloads, organizations can optimize accuracy, groundedness, operational cost, and long-term maintainability while building AI systems that remain reliable as business needs evolve.

Whether you’re building an enterprise knowledge assistant, an AI copilot, a document intelligence platform, or a task-specific LLM application, choosing the right customization strategy is critical for long-term success.

At Digis, we help organizations design, build, and optimize production-ready Generative AI solutions using Retrieval-Augmented Generation, LLM fine-tuning, hybrid AI architectures, AI agents, and enterprise knowledge platforms. Our engineering teams support the complete lifecycle—from architecture design and evaluation to deployment, observability, and continuous optimization.

If you’re deciding between RAG, fine-tuning, or a hybrid approach, contact Digis to discuss your AI Development project and identify the architecture that best fits your business goals.

Summary

FAQ: RAG vs Fine-Tuning

Is RAG Better Than Fine-Tuning?

Neither approach is universally better. RAG is generally the preferred choice when applications depend on current, private, or verifiable knowledge. Fine-tuning is better suited to consistent model behavior, structured outputs, and specialized repetitive tasks. The right decision depends on the specific business problem and should be validated through representative evaluations.

Is Fine-Tuning More Expensive Than RAG?

Cost depends on the model, dataset size, infrastructure, and expected workload. Fine-tuning introduces training and model lifecycle costs, while RAG requires retrieval infrastructure, vector databases, embedding generation, and document indexing. Organizations should compare the total cost of ownership rather than implementation costs alone.

Can RAG and Fine-Tuning Be Used Together?

Yes. Modern enterprise AI systems frequently combine both approaches. RAG provides current, source-backed knowledge during inference, while a fine-tuned model delivers specialized behavior, consistent formatting, or domain-specific reasoning. Hybrid architectures should be adopted only when measurable quality improvements justify the additional operational complexity.

Should You Start with RAG or Fine-Tuning?

For most enterprise applications, start with prompt engineering and evaluate whether the primary limitation is missing knowledge or inconsistent behavior. Choose RAG when current information is the main requirement. Consider fine-tuning when behavior remains inconsistent despite strong prompts and representative examples. Hybrid architectures should be introduced incrementally.