The RAG Engineering Journey: Architecture, Evaluation, Performance, and Lessons from an Experimental Laboratory

Retrieval-Augmented Generation (RAG) has become an important architectural approach for connecting large language models (LLMs) with external knowledge sources. By retrieving relevant information before generating a response, RAG systems seek to improve factual grounding, reduce dependence on model-internal knowledge, and provide traceability to supporting documents.

However, the apparent simplicity of the RAG concept conceals a complex engineering problem.

A basic implementation may require only a document collection, an embedding model, a vector database, and a generative language model. Yet the presence of these components does not establish that the resulting system retrieves the correct evidence, generates faithful answers, performs efficiently, or behaves consistently across different questions.

These properties must be investigated independently.

This article examines RAG as an engineering discipline through an experimental laboratory involving structured technical documentation, local language models, semantic retrieval, automated evaluation, and system observability.

The investigation focuses on seven interconnected dimensions:

1. The fundamental engineering principles behind RAG systems.
2. Architectural decomposition from source documents to grounded answers.
3. Retrieval evaluation using Recall, Precision, and Mean Reciprocal Rank.
4. The limitations of reranking as a retrieval optimization technique.
5. Performance analysis and the computational cost of language generation.
6. Automated testing and the interpretation of code coverage.
7. Observability as a foundation for understanding system behavior.

Rather than treating RAG as a single AI capability, the objective is to examine it as a measurable information-processing system.

The central finding is that a functioning RAG pipeline is not equivalent to a validated RAG system.

A pipeline can execute successfully while retrieving incomplete evidence, introducing irrelevant context, generating unsupported statements, or consuming excessive computational resources.

Understanding these distinctions is essential for rigorous RAG engineering.

1. RAG as an Engineering Problem

1.1 The limitations of traditional document search

Technical knowledge is frequently distributed across multiple documents containing procedures, architectural descriptions, configuration parameters, examples, diagrams, and operational explanations.

Traditional keyword search can identify documents containing specific terms, but it often struggles when users formulate questions using terminology that differs from the source material.

Consider a collection of technical manuals describing software configuration and system administration.

A user might ask:

How can I configure a component to process incoming data automatically?

The relevant documentation may describe the same functionality using different terminology, such as event processing, scheduled execution, or workflow configuration.

A purely lexical search mechanism may fail to recognize these relationships.

Semantic retrieval attempts to address this limitation by representing text as numerical vectors that capture aspects of its meaning.

However, semantic similarity introduces its own uncertainty.

Two passages may discuss closely related concepts without containing the information necessary to answer a particular question.

Consequently, semantic relevance and answer sufficiency must be treated as different properties.

1.2 Why RAG is more than semantic search

A RAG system combines information retrieval with language generation.

The retrieval component identifies potentially relevant evidence, while the generative component transforms that evidence into a natural-language response.

Conceptually:

User Question → Retrieval → Relevant Context → LLM → Generated Answer

This abstraction is useful, but incomplete.

A more rigorous engineering model must account for document preparation, chunking, indexing, ranking, context selection, source attribution, testing, and telemetry.

Errors introduced at an early stage may propagate through the entire pipeline.

For example, if document conversion removes a critical configuration parameter, neither the embedding model nor the language model can reliably reconstruct the missing evidence.

Similarly, if retrieval selects an incorrect passage, the generative model may produce a fluent but unsupported answer.

This leads to the first engineering principle:

The quality of a RAG system depends on the integrity of the complete information pipeline, not merely on the capabilities of its language model.

2. RAG Architecture: From Documents to Grounded Answers

A RAG architecture can be divided into two major processes: knowledge preparation and question answering.

Although these processes interact, they have different computational characteristics and validation requirements.

2.1 Knowledge preparation

The knowledge preparation process transforms source documents into searchable representations.

The experimental laboratory used a collection of 11 structured technical guides containing explanatory text, hierarchical sections, configuration examples, code fragments, and references to images.

The processing sequence was:

Source Documents → Format Conversion → Structural Extraction → Chunking → Embeddings → Vector Index

Each stage introduces a distinct engineering concern.

Document conversion

Source documents must be transformed into a representation suitable for automated processing.

In the laboratory, structured documentation was converted through intermediate formats before being normalized into Markdown.

The objective was not simply to extract readable text.

It was to preserve the relationships between headings, paragraphs, examples, and technical references.

A successful conversion operation does not necessarily preserve semantic integrity.

For example, a document may remain readable while losing the relationship between a configuration instruction and the warning that qualifies it.

This distinction is especially important in technical documentation.

Structural extraction

Technical manuals typically contain hierarchical information.

A section may belong to a chapter, which belongs to a larger conceptual area.

Preserving this hierarchy provides useful retrieval metadata.

A passage describing a configuration parameter becomes more meaningful when its parent section and document are known.

Structural information also improves traceability by allowing the system to identify where retrieved evidence originated.

Chunking

Embedding entire documents as single vectors is generally unsuitable for detailed question answering.

Large documents contain multiple subjects, making it difficult for a single vector representation to preserve fine-grained relevance.

Chunking divides documents into smaller searchable units.

However, chunk size involves a trade-off.

Very small chunks may omit necessary context. Very large chunks may contain unrelated information and reduce retrieval precision.

The laboratory adopted a hierarchical segmentation strategy based on document structure.

The resulting corpus contained:

These figures describe the experimental dataset. They do not independently establish retrieval quality.

2.2 The role of images and document references

Technical knowledge is not exclusively textual.

Diagrams, screenshots, and illustrations may contain information that complements written instructions.

The laboratory identified 288 image references and resolved their associated resources.

This required treating images as part of the document-processing problem rather than ignoring them during text extraction.

Nevertheless, resolving an image reference does not establish that the image is relevant to a particular question.

Image availability, image relevance, and visual answer quality are separate properties.

The experiment therefore supports an important distinction:

Resource integrity is necessary for reliable multimodal document handling, but it is not evidence of multimodal retrieval accuracy.

2.3 Embedding generation

After segmentation, each chunk is transformed into a vector representation using an embedding model.

The laboratory employed the locally executed nomic-embed-text model through Ollama.

Embedding models enable similarity-based retrieval by mapping text into a numerical space.

However, embeddings do not guarantee that a retrieved passage contains a complete or correct answer.

They provide a mechanism for ranking semantic relationships.

The distinction matters because a passage can be highly similar to a question while omitting a critical instruction or condition.

2.4 Vector storage and retrieval

The laboratory used ChromaDB to store vector representations and support similarity searches.

When a question is submitted, the system generates a query embedding and searches for related document chunks.

A configurable retrieval parameter, commonly called top-K, determines how many results are returned.

Increasing K can improve the probability of including relevant evidence, but it may also introduce more irrelevant material.

This trade-off becomes measurable through retrieval evaluation.

2.5 Context assembly and answer generation

Retrieved passages are assembled into a context supplied to the language model.

The laboratory used a locally executed Qwen3 4B model for answer generation.

The language model receives the question and selected supporting passages, then produces a natural-language response.

Source metadata can be attached to the response to identify the documents and sections used.

This is the foundation of grounded generation.

However, an answer containing citations is not necessarily faithful to those citations.

A language model may still misinterpret a passage, omit an important qualification, or introduce information not supported by the retrieved material.

Therefore, retrieval relevance, citation availability, and answer faithfulness require separate evaluation.

3. Evaluating RAG Retrieval: Recall, Precision, and MRR

A RAG system should not be evaluated solely by observing whether it produces plausible answers.

Retrieval evaluation examines whether the system identifies the evidence needed to answer a question.

The laboratory used a manually labeled evaluation dataset containing 30 questions.

Each question was associated with an expected relevant document section.

The experiment measured retrieval performance at different values of K.

3.1 Recall@K

Recall measures the proportion of relevant items successfully retrieved.

In general:

Recall = Relevant Retrieved Items / Total Relevant Items

Recall@K restricts the calculation to the first K retrieval results.

In the laboratory's evaluation, each question was associated with one expected target section. Therefore, Recall@K represented the proportion of questions for which the expected section appeared within the first K results.

The observed results were:

The results show that increasing K improved the probability of retrieving the expected section.

However, this improvement should not be interpreted as proof that all information required for every answer was retrieved.

The evaluation measured retrieval against labeled target sections, not the completeness of every generated answer.

3.2 Precision@K

Precision measures the proportion of retrieved results considered relevant.

Precision = Relevant Retrieved Items / Total Retrieved Items

The experiment produced the following results:

Precision decreased as K increased.

This behavior is understandable within the evaluation design.

When each question has a limited number of labeled relevant sections, retrieving more results increases the denominator while introducing additional passages that are not labeled relevant.

Consequently, a larger retrieval set may improve Recall while reducing Precision.

This demonstrates an important engineering trade-off:

Retrieving more context can increase evidence coverage without improving the concentration of relevant information.

A larger context window is not automatically a better context window.

3.3 Mean Reciprocal Rank

Mean Reciprocal Rank (MRR) measures how early the first relevant result appears in the ranking.

For each query, reciprocal rank is calculated as:

Reciprocal Rank = 1 / Rank of the First Relevant Result

MRR is the average reciprocal rank across the evaluated questions.

The laboratory obtained:

These values indicate that relevant results frequently appeared near the beginning of the ranking.

The evaluation also showed that the expected section appeared first for 20 of the 30 questions.

This is meaningful because users and downstream language models are often sensitive to the order in which evidence is presented.

Nevertheless, MRR measures ranking effectiveness, not the factual correctness of generated responses.

3.4 What the metrics actually demonstrate

The combined results reveal a retrieval system with relatively strong target-section coverage and useful ranking behavior under the tested conditions.

They also reveal an important limitation.

Higher Recall was accompanied by lower Precision.

Therefore, choosing K requires balancing the probability of finding relevant evidence against the amount of potentially distracting context.

The laboratory results support conclusions about the tested retrieval configuration.

They do not establish general performance across all technical domains, document collections, or question types.

Furthermore, the evaluation dataset was relatively small.

The reported measurements should be interpreted as experimental baseline results rather than universal performance expectations.

4. When Reranking Makes RAG Worse

Reranking is frequently proposed as a way to improve retrieval quality.

The idea is straightforward.

An initial retrieval stage selects candidate passages, and a second model reorders them according to their estimated relevance to the question.

A common implementation uses a cross-encoder model that evaluates the query and candidate passage together.

In principle, this can produce a more accurate ranking than vector similarity alone.

However, the additional model introduces computational cost and does not guarantee better results.

4.1 Experimental comparison

The laboratory evaluated a cross-encoder reranking approach using ms-marco-MiniLM-L-6-v2.

The experiment compared the original retrieval configuration against the reranked results.

The results did not support adopting the tested reranking configuration.

Recall remained unchanged, while Precision and MRR decreased slightly.

At the same time, mean retrieval time increased by approximately 20 times.

4.2 Why can reranking reduce effectiveness?

Several explanations are technically plausible.

• First, the reranking model may not be well aligned with the terminology and structure of the target documents.
• Second, the original vector retrieval may already provide a sufficiently strong ranking for the evaluated questions.
• Third, the reranker may assign higher scores to semantically related passages that are not the labeled target sections.
• Fourth, the limited evaluation dataset may make small ranking differences appear more consequential than they would be in a broader study.

These are hypotheses, not experimentally established causes.

The available measurements demonstrate the observed outcome, but they do not isolate which mechanism produced it.

4.3 The engineering lesson

Reranking should be evaluated as a hypothesis.

The hypothesis is not simply that an additional model will improve retrieval.

It is that a specific reranking model, applied to a specific corpus and retrieval configuration, will produce a measurable improvement that justifies its computational cost.

In this experiment, that hypothesis was not supported.

The tested reranking approach was therefore excluded from the selected baseline.

The broader lesson is that architectural complexity must be justified by evidence.

Adding an AI component does not necessarily improve an AI system.

5. Measuring RAG Performance: Why Generation Becomes the Bottleneck

Performance analysis is another essential dimension of RAG engineering.

A RAG response may involve several computational stages:

• Query embedding.
• Vector retrieval.
• Context preparation.
• Language-model generation.
• Response formatting and metadata handling.

Measuring only total response time conceals the contribution of each stage.

5.1 Observed latency distribution

One instrumented laboratory observation recorded the following measurements:

In this observation, language generation accounted for approximately 97.4% of total response time.

The result demonstrates a major imbalance between retrieval and generation costs.

The vector search completed in milliseconds, while the language model required more than 21 seconds.

These measurements represent a specific observed execution, not a statistical distribution of all requests.

5.2 Why does language generation dominate?

Language generation differs computationally from vector similarity search.

Vector retrieval operates over previously generated representations.

By contrast, an autoregressive language model generates output tokens sequentially.

Its execution time can depend on several factors:

• Model architecture and parameter count.
• Available computational resources.
• Input context length.
• Number of generated tokens.
• Inference configuration.
• Model loading and runtime behavior.

The laboratory used local inference, making hardware constraints particularly relevant.

However, the available measurements do not isolate the contribution of each factor.

The supported conclusion is narrower: generation dominated the measured end-to-end latency.

5.3 Why optimizing retrieval may have little effect

Suppose a system spends 50 milliseconds retrieving documents and 20 seconds generating an answer.

Reducing retrieval time by half saves approximately 25 milliseconds.

That improvement may be technically valid, but its impact on total response time is negligible.

This illustrates the importance of performance attribution.

Optimization should focus on the operations responsible for the largest measurable costs.

Without stage-level timing, engineering teams may invest effort in components that have little influence on the overall experience.

5.4 Performance must be evaluated alongside quality

A faster answer is not necessarily a better answer.

Reducing context length may improve generation speed but remove necessary evidence.

Reducing output length may lower latency but produce incomplete explanations.

Using a smaller model may reduce computational requirements while changing answer quality.

Therefore, performance evaluation should not be isolated from retrieval and generation quality.

The engineering objective is not merely minimum latency.

It is an acceptable balance among latency, computational cost, evidence relevance, and answer quality.

6. Testing RAG Applications: What 95% Code Coverage Really Means

Automated testing is essential for the software components surrounding a RAG system.

However, conventional software testing and AI quality evaluation answer different questions.

The laboratory reported 51 passing automated tests and approximately 95% instruction coverage in its measured application scope.

These results provide evidence about the execution of tested software paths.

They do not establish that generated answers are accurate.

6.1 What code coverage measures

Code coverage indicates which portions of a program were executed during testing.

Depending on the coverage method, it may measure statements, branches, functions, or other execution units.

A high coverage percentage suggests that the test suite exercises a substantial portion of the measured code.

This is useful for identifying untested application logic.

For example, tests can verify that:

• Document-processing functions handle expected inputs.
• Metadata is stored and retrieved correctly.
• Invalid parameters produce controlled errors.
• Retrieval interfaces return properly structured results.
• API responses follow defined schemas.
• Telemetry records contain expected fields.

These are conventional software engineering concerns.

6.2 What code coverage does not measure

A test can execute a function without verifying that the function produces the correct semantic result.

This limitation becomes especially important in RAG systems.

Consider a test that submits a question and verifies that the response contains text.

The test may pass even if the answer is factually incorrect.

Similarly, a retrieval test may confirm that three passages were returned without checking whether any of them are relevant.

Therefore, 95% code coverage does not imply:

• 95% retrieval accuracy.
• 95% factual correctness.
• 95% answer faithfulness.
• 95% resistance to hallucination.
• 95% completeness of the underlying knowledge base.

These quantities are conceptually different.

6.3 A multidimensional quality model

A more rigorous approach separates RAG quality into multiple dimensions.

The experimental laboratory established automated software tests and retrieval evaluation.

However, the reported evidence does not establish a comprehensive quantitative benchmark for answer faithfulness.

That remains a distinct evaluation requirement.

6.4 Why passing tests are not enough

Traditional software behavior is often deterministic.

Given the same input and system state, a function is expected to produce the same output.

Generative AI introduces additional variability.

Two answers may use different wording while expressing the same valid information.

Conversely, two fluent answers may differ in factual correctness.

This means that exact string matching is often insufficient for evaluating generated responses.

RAG testing requires both deterministic software tests and semantic evaluation methods.

The lesson is straightforward:

High code coverage demonstrates testing effort and execution breadth. It does not establish the epistemic reliability of an AI-generated answer.

7. RAG Observability: Understanding Retrieval, Generation, and System Behavior

Observability provides the evidence required to explain how a system behaves.

In a RAG application, a successful response does not reveal whether retrieval was efficient, which passages were selected, or how much time the language model consumed.

Without telemetry, these questions are difficult to answer consistently.

7.1 Observability across the RAG pipeline

The laboratory incorporated instrumentation for important execution stages.

Its architecture included metadata persistence and telemetry records supporting analysis of query execution.

The observed instrumentation covered aspects such as:

• Retrieval and generation timing.
• Query execution metadata.
• Token-related measurements where available.
• System events.
• Execution observations.

The experimental records included 13 query observations and 65 telemetry events at the documented checkpoint.

These counts demonstrate the existence of recorded observations, not the completeness of every possible monitoring dimension.

7.2 Why stage-level telemetry matters

Suppose a user receives an answer after 25 seconds.

Without instrumentation, the delay could be attributed to several components.

Possible explanations include document retrieval, context construction, language-model inference, or application overhead.

Stage-level telemetry makes these hypotheses testable.

In the laboratory, timing measurements identified language generation as the dominant cost in a sampled execution.

That conclusion would not have been possible from total latency alone.

7.3 Observability and evaluation are complementary

Observability describes what happened during execution.

Evaluation determines whether the observed behavior satisfies defined quality criteria.

For example, telemetry may record that retrieval completed in 10 milliseconds.

That measurement does not establish whether the retrieved passages were relevant.

Similarly, a retrieval evaluation may demonstrate high Recall@K without explaining why a particular response required 30 seconds.

Both perspectives are necessary.

Observability provides execution evidence; evaluation provides quality evidence.

7.4 The importance of reproducibility

Experimental results become more useful when the conditions under which they were obtained are recorded.

Relevant metadata may include:

• Model identifiers.
• Retrieval configuration.
• Top-K values.
• Evaluation dataset version.
• Document corpus version.
• Timing measurements.
• Test execution results.

These records support comparisons between alternative configurations.

Without reproducibility, an apparent improvement may reflect changes in the dataset, model behavior, or experimental conditions rather than the modification being evaluated.

The laboratory's recorded metrics and test artifacts provide a foundation for this type of analysis.

However, not every quality dimension was fully instrumented or evaluated at the documented checkpoint.

8. An Integrated Interpretation of the Experimental Results

The most important conclusions emerge when the individual measurements are considered together.

8.1 Retrieval coverage and context quality are not equivalent

The laboratory achieved Recall@10 of 100% against its labeled evaluation targets.

However, Precision@10 was only 15.33%.

These results are not contradictory.

They indicate that the expected target sections were found within the first ten results, while many retrieved passages were not labeled relevant.

The implication is that increasing retrieval depth can improve coverage while reducing the proportion of relevant evidence.

8.2 Better ranking models do not guarantee better rankings

The reranking experiment introduced an additional model and significantly increased retrieval time.

Yet the evaluated ranking metrics did not improve.

This demonstrates why optimization techniques should be treated as experimental interventions rather than assumed improvements.

8.3 Retrieval speed does not determine total response speed

The measured vector retrieval time was approximately 2.56 milliseconds.

The measured language-generation time exceeded 21 seconds.

This difference demonstrates that optimizing a fast retrieval component may have little effect when generation dominates end-to-end latency.

8.4 Software quality and answer quality require different evidence

The laboratory reported 51 passing tests and approximately 95% instruction coverage.

These results are valuable for assessing tested software behavior.

However, they cannot be substituted for measurements of answer correctness, factual grounding, or hallucination frequency.

8.5 Traceability does not automatically establish faithfulness

Source references make it possible to inspect the evidence associated with an answer.

They do not guarantee that the answer accurately represents that evidence.

A rigorous RAG evaluation must examine both source selection and the relationship between sources and generated statements.

9. Seven Engineering Lessons from the RAG Laboratory

The experimental findings support seven broader principles.

Lesson 1: Document engineering precedes AI engineering

The quality of document conversion, segmentation, and metadata preservation directly influences the information available to retrieval and generation.

A language model cannot reliably compensate for missing source information.

Lesson 2: RAG architecture must be evaluated component by component

A single end-to-end success indicator is insufficient.

Document processing, retrieval, generation, testing, and observability require distinct validation criteria.

Lesson 3: Retrieval metrics reveal trade-offs, not universal answers

Increasing K improved Recall but reduced Precision in the evaluated dataset.

Retrieval configuration should therefore reflect the information requirements and evaluation objectives of the application.

Lesson 4: Additional models must justify their computational cost

The tested reranking model increased latency without improving the selected retrieval metrics.

Complexity should be introduced only when evidence supports its value.

Lesson 5: Performance optimization requires attribution

The laboratory's timing measurements showed that generation dominated the sampled response latency.

Optimization priorities should follow measured bottlenecks rather than architectural assumptions.

Lesson 6: Code coverage is not an AI accuracy metric

Passing tests and high instruction coverage provide useful software quality evidence.

They do not establish factual correctness or answer faithfulness.

Lesson 7: Observability turns system behavior into engineering evidence

Without recorded measurements, performance and quality discussions remain speculative.

Telemetry, evaluation datasets, and reproducible experiments make it possible to investigate the system systematically.

10. Toward a More Rigorous RAG Engineering Methodology

The laboratory findings suggest that RAG engineering benefits from a disciplined experimental methodology.

A useful conceptual sequence is:

Define → Prepare → Retrieve → Evaluate → Generate → Measure → Compare

Each stage addresses a different question.

Define: What information problem must the system solve?

Prepare: Is the source material complete, structured, and suitable for retrieval?

Retrieve: Can the system identify evidence relevant to a question?

Evaluate: How effectively does retrieval perform against labeled expectations?

Generate: Can the language model transform the retrieved evidence into a useful answer?

Measure: What are the computational costs and observable behaviors?

Compare: Do architectural changes improve measurable outcomes?

This methodology encourages evidence-based decisions.

It also discourages the assumption that newer models, larger contexts, or additional processing stages automatically produce better results.

A RAG system should be understood as a combination of information retrieval, document engineering, language modeling, and software quality practices.

Its effectiveness emerges from the interaction of these disciplines.

Frequently Asked Questions

What is the most important component of a RAG system?

There is no universally dominant component.

Document quality, retrieval effectiveness, context construction, and language generation all influence the result.

A strong language model cannot reliably compensate for missing or irrelevant evidence.

Does increasing Top-K always improve RAG accuracy?

No.

Increasing K may improve the probability of retrieving relevant evidence, but it can also introduce unrelated passages.

The laboratory observed increasing Recall and decreasing Precision as K increased.

These retrieval measurements do not directly establish answer accuracy.

Is reranking necessary for every RAG application?

No.

Reranking is an optional technique that should be evaluated against a baseline.

In the laboratory experiment, the tested reranking model increased retrieval time without improving the selected quality metrics.

Why can a RAG system be slow when vector search is fast?

Vector retrieval and language generation have different computational characteristics.

In one laboratory observation, language generation accounted for approximately 97.4% of total response time.

Does 95% code coverage mean a RAG system is 95% accurate?

No.

Code coverage measures execution of software instructions during testing.

Answer accuracy, retrieval relevance, and factual grounding require separate evaluation methods.

What is the difference between RAG evaluation and observability?

Evaluation measures whether system behavior satisfies defined quality criteria.

Observability records what happened during execution.

Both are necessary to understand system effectiveness.

Can a RAG system provide citations and still generate incorrect answers?

Yes.

Citations identify associated sources, but they do not guarantee that generated statements accurately reflect those sources.

Answer faithfulness must be evaluated independently.

Conclusion: RAG Engineering Is an Evidence-Based Journey

Retrieval-Augmented Generation is often presented as a straightforward method for combining document search with large language models.

The experimental evidence examined in this article reveals a more complex reality.

Document structure influences retrieval. Retrieval configuration affects evidence coverage and precision. Reranking can introduce additional cost without improving results. Language generation can dominate response latency. High code coverage can coexist with unresolved questions about factual accuracy.

These findings demonstrate why RAG systems should be studied through multiple engineering disciplines rather than evaluated as isolated AI applications.

The central lesson is not that a particular architecture, model, or retrieval strategy is universally superior.

It is that RAG quality must be demonstrated through reproducible experiments, appropriate metrics, and observable system behavior.

A system that generates an answer has demonstrated functionality.

A system that retrieves relevant evidence has demonstrated a measurable retrieval capability.

A system that produces faithful answers consistently requires additional evidence.

Recognizing these distinctions is a fundamental step toward rigorous RAG engineering.

Coming Next: Part II — Why RAG Systems Fail in Real-World Environments

This article establishes the experimental foundations of RAG architecture, evaluation, testing, performance, and observability.

The second article in this series will examine a different question:

Why do RAG systems that perform well in controlled experiments sometimes fail when exposed to real-world requirements?

That discussion will explore the transition from experimental success to practical reliability, including the challenges that emerge when RAG applications move beyond the laboratory.

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