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A dependable retrieval-augmented generation (RAG) system is a complete evidence pipeline, not just an embedding model connected to a vector index. It must prepare and update source documents, retrieve the right passages for real queries, assemble enough context, and produce answers that can be checked against those passages. For Thai content, preserve meaningful spaces and test segmentation explicitly; neither a universal chunk size nor a Thai-specific performance gain is established by the cited work.
What RAG adds—and what it does not guarantee
RAG combines a language model with information retrieved from an external collection. That collection can be inspected and updated independently of the model’s learned, or parametric, memory. In the foundational 2020 paper by Lewis and colleagues, the system paired a pretrained sequence-to-sequence generator with a neural retriever that accessed a dense vector index of Wikipedia. The researchers compared conditioning generation on the same retrieved passages across an output with allowing different passages to inform different tokens. On the knowledge-intensive NLP and question-answering tasks they evaluated, their system outperformed parametric-only baselines on several QA tasks; those findings describe that experimental setup, not a guarantee for every application.
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Retrieved evidence can improve grounding, but RAG does not eliminate hallucinations. A system may retrieve the wrong source, omit the needed passage, or generate an answer that misstates evidence it did retrieve. Keep the source and passage provenance available so a reviewer—or the application itself—can check what supports an answer. A plausible response alone does not show that retrieval succeeded.
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How documents become answers: two connected pipelines
A useful production design separates work done when source material changes from work done for each user query. IBM’s 2024 architecture guide describes the major stages as data preparation, ingestion and storage or embedding, followed online by prompting, retrieval and generation. The division makes it easier to find where a failure originates and to control updates without treating the model call as the whole system.
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Offline: prepare, index, and maintain source material
- Collect and parse documents. Extract readable content from the formats your corpus actually contains. Preserve useful structure—such as headings, paragraphs, lists and tables—rather than flattening everything into undifferentiated text.
- Clean without erasing meaning. Remove artifacts that impede retrieval, but retain meaningful boundaries, language-specific spacing and metadata. Record source identity and other metadata the application needs for filtering, attribution or updates.
- Split into retrievable units. Choose boundaries and sizes that suit the documents and the questions people ask. Chunking is an indexing decision: it determines what can be retrieved together and how much surrounding material arrives with a match.
- Embed and index. Convert chunks into representations usable by the selected retrieval approach, then store those representations alongside the source text and its provenance. A vector database is one possible implementation, not a requirement for every RAG design; IBM notes that retrieval approaches may call for different database types.
- Plan for change. Keep source-to-chunk relationships and a process for reprocessing changed documents. Otherwise, updated source material can coexist with stale indexed evidence.
Online: retrieve evidence and formulate an answer
- Accept and interpret the query. Apply the query handling, filters or language-aware processing required by the application.
- Retrieve candidates. Search the index for potentially relevant passages. Retrieval strategy and filters determine which candidates are eligible and which rise in ranking.
- Select and assemble context. Choose passages that fit the context budget while preserving the evidence and surrounding detail needed to answer. A strong match can still be inadequate if a key qualification lives elsewhere.
- Prompt the model with evidence. Provide the original query, the selected passages and application instructions. IBM’s architecture example uses this pattern; the instructions should make clear how the model is expected to use the supplied material.
- Generate and retain provenance. Return an answer in the required format and retain, or expose, the source passages used so its support can be reviewed.
Where a RAG answer can fail
Separate retrieval quality from answer quality when diagnosing the system. A missing or wrong answer can begin before generation: a document may be absent, parsed badly, split so its relevant facts are separated, embedded inadequately, filtered out, or ranked below the context cutoff. Alternatively, the necessary evidence may reach the prompt and the model may still ignore or misrepresent it. Logging retrieved passage identifiers and the assembled context helps distinguish these cases; judging only the final prose does not.
How to choose chunking for a real corpus
There is no best chunk size established for all RAG systems. The 2025 Findings of ACL paper “Document Segmentation Matters for Retrieval-Augmented Generation” describes the central tradeoff: large chunks can bring irrelevant material into retrieval and generation, while very small chunks can omit semantic context needed for a coherent answer. Fixed-length or rule-based splitting is common, and semantic grouping is one research direction.
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Compare chunking approaches against your own documents and questions rather than selecting a token count by convention. Start with a structural baseline, then test bounded-size splitting or more elaborate semantic or hierarchical methods when the likely benefit justifies their extra complexity. Where possible, hold retrieval and generation conditions constant so the comparison isolates chunking. Inspect whether retrieved passages contain complete answers and how much irrelevant text accompanies them.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problems| Approach | What it does | Evidence and practical consideration |
|---|---|---|
| Structure-aware baseline | Uses document structure, such as headings and paragraphs, as meaningful boundaries. | A sensible design baseline, not a method with a universal measured advantage in the cited sources. Check whether units remain answer-complete. |
| Fixed-length or rule-based splitting | Divides text according to set size or rules. | Common approaches discussed in the 2025 ACL Findings paper. Large units risk irrelevant context; small units risk missing needed context. |
| Semantic grouping with PIC | The 2025 paper’s PIC method uses document summaries as pseudo-instructions and groups sentences by semantic similarity to the summary. | The paper reports improvements on multiple open-domain QA benchmarks in Hits@k and exact match without additional training. This is a reported result on those benchmarks, not a general guarantee or a Thai-specific finding. |
| Hierarchical structuring with Auto-Merge | HiChunk presents hierarchical document structuring and Auto-Merge retrieval. | The 2026 ACL paper introduces HiCBench with manually annotated multilevel chunk boundaries, evidence-dense QA pairs and corresponding evidence sources. Consider the approach in light of workload fit; the benchmark is not evidence that it matches every corpus or Thai workload. |
Use the same question set and, as far as possible, the same retrieval and generation settings for each candidate. Compare evidence completeness, retrieval precision, structural fidelity, language fit, index and query cost, and update behavior. These are practical engineering criteria, not a published scorecard or results from a system test.
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How to chunk Thai text without losing useful boundaries
Do not assume that Thai words are separated by spaces in the same way as English. The 2021 WangchanBERTa paper states: “We apply text processing rules that are specific to Thai most importantly preserving spaces, which are important chunk and sentence boundaries in Thai before subword tokenization.” Its authors also explored SentencePiece, dictionary-based word-level and syllable-level tokenizers—including PyThaiNLP’s newmm—and another tokenizer on Thai Wikipedia data.
For a Thai RAG corpus, preserve meaningful spaces and document structure during parsing rather than stripping spacing as generic cleanup. Then inspect real segmentation around clauses, headings, lists, tables and Thai-English code switches. If a tokenizer or segmenter defines chunk boundaries, record its version and test domain vocabulary, spelling variation, numerals and mixed-script terms. These are engineering checks inferred from the paper’s preprocessing findings; the paper is not a production RAG comparison across these cases.
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The WangchanBERTa authors report a cleaned and deduplicated training set of 78 GB. That figure describes their model’s pretraining corpus, not a recommended RAG corpus size, chunk size or benchmark result. The cited Thai-specific evidence concerns language-model preprocessing, not an end-to-end test showing that one Thai RAG chunking strategy outperforms another by a particular percentage.
Evaluate the evidence path, not just chunk boundaries
Build an evaluation set from representative questions and label which source evidence is needed for each answer. Include cases the corpus cannot answer, as well as Thai and mixed Thai-English queries. For each case, inspect whether retrieval found the required evidence, whether the assembled context is sufficient, and whether the generated answer is supported by the supplied passages. This lets teams tell a retrieval failure from an answer-generation failure without assuming a universal threshold.
Chunk boundaries alone are an incomplete measure. Lu and colleagues’ 2026 ACL paper, “HiChunk: Evaluating and Enhancing Retrieval Augmented Generation with Hierarchical Chunking,” argues that evidence sparsity can make existing RAG benchmarks inadequate for evaluating chunking. Its HiCBench pairs multilevel annotated boundaries with evidence-dense questions and evidence sources. That work makes evidence-oriented evaluation an active research issue; it does not establish that its benchmark represents every production corpus.
Quick Recap
Production readiness checklist
- Source integrity: Can you identify the original document behind every indexed passage and preserve useful structure during parsing?
- Update path: Can changed or removed source documents be reflected in the index without leaving stale evidence?
- Retrieval behavior: Are search strategy, filters and the context cutoff appropriate for the workload, and can you inspect what was retrieved?
- Answer support: Can users or reviewers trace claims to passages, and does the application handle insufficient evidence rather than treating every query as answerable?
- Thai handling: Are meaningful spaces preserved, and have Thai and code-switched examples been checked through parsing, segmentation, retrieval and answer generation?
- Workload evaluation: Do test questions reflect actual documents and user needs, include unanswerable cases, and assess evidence sufficiency as well as final answers?
- Operational fit: Does the chosen search or database infrastructure meet the system’s retrieval, filtering, maintenance and cost requirements? The architecture sources support treating the storage and search layer as a workload-specific choice, not a mandated product.
- Failure diagnosis: Can you tell whether a bad result came from absent or malformed source content, chunking, ranking, context selection or generation?
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