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A production RAG system is more than embeddings and a vector search call. It depends on a maintainable document pipeline, retrieval that finds the right evidence, context assembly that preserves what matters, and generation that uses that evidence appropriately. There is no universally best chunk size; choose and validate chunking against your corpus, language mix, and real questions.
What RAG adds—and what it does not guarantee
Retrieval-augmented generation (RAG) combines a language model with external information that can be inspected and updated separately from 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 and a dense vector index of Wikipedia. The authors studied knowledge-intensive NLP tasks and reported gains over parametric-only baselines on several evaluated question-answering tasks. Those results describe that research setup; they do not establish that every RAG application will improve.
Retrieved passages give an answer a potential evidence base, not a guarantee of truth or faithful use. The system may retrieve the wrong material, or the generator may ignore or misrepresent relevant passages. A plausible answer alone does not show that retrieval succeeded. Preserve source and passage provenance so an answer can be checked against the material supplied to the model.
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Think of RAG as two connected pipelines: an offline path that prepares and indexes knowledge, and an online path that finds and uses it. IBM’s 2024 architecture guide describes these broad stages, including preprocessing, ingestion, storage and embedding, prompting, retrieval, and generation. The particular database and search approach are implementation choices: a vector database is common in general RAG designs, but it is not the only possible storage or retrieval arrangement.
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Offline: prepare and maintain the knowledge base
- Collect and parse source documents. Extract usable text while preserving structure that carries meaning, such as headings, paragraph relationships, lists, and tables. Poor extraction can make later retrieval fail even if the source document itself contains the answer.
- Clean and enrich the content. Remove noise without discarding meaningful wording or boundaries. Attach useful metadata, such as document identity and section information, so retrieved passages can be filtered, interpreted, and traced back to their sources.
- Split the material into chunks. Choose units that balance enough context to answer a question against unnecessary surrounding text. The chunk is the unit the system can index and retrieve, so its boundaries affect both relevance and completeness.
- Embed and index. Convert the prepared chunks into representations used by the selected retrieval system, then store them with the text and metadata needed to assemble evidence later.
- Plan for source changes. Keep the relationship between source documents and indexed content clear. When documents change, the system needs a way to reprocess and re-index affected material; otherwise, it can return stale evidence.
Online: retrieve evidence and generate an answer
- Accept and interpret the query. Preserve the user’s wording and apply any query handling your application requires.
- Retrieve candidate passages. Search the indexed material and apply relevant filters or other retrieval logic. Retrieval quality depends on what was parsed, how it was split and represented, and how candidates are ranked.
- Select and assemble context. Choose passages that together contain the evidence needed to answer, while respecting the context limit and avoiding unnecessary noise.
- Prompt the model. Supply the query, selected passages, and application instructions. IBM’s architecture example uses this pattern: original query, retrieved passages, and an instruction prompt are sent to the language model.
- Generate and retain provenance. Produce an answer using the assembled evidence and retain the passages or source references used, so the result can be checked.
Why retrieval and answer quality must be evaluated separately
Failures can occur at different points in the evidence path. The answer may be absent from the corpus, lost or distorted during parsing, split across unsuitable chunks, poorly represented for search, excluded by filters, or ranked below the context cutoff. Even when the required evidence reaches the prompt, generation can still overlook it or state it inaccurately.
Track whether retrieval surfaced the required evidence separately from whether the final answer is supported by that evidence. This distinction helps diagnose whether to improve source preparation, chunking, search, context selection, or generation behavior instead of treating every wrong answer as the same problem.
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How to choose a chunking method
Chunking is a retrieval design decision, not a setting with one correct value for every system. The 2025 Findings of ACL paper “Document Segmentation Matters for Retrieval-Augmented Generation” describes the central tradeoff: large chunks can bring irrelevant information into retrieval and generation, while small chunks can omit the semantic context required for a coherent answer. Fixed-length or rule-based segmentation is common; semantic grouping is one direction being studied.
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Compare methods against the workload
| Comparison axis | Question for the team |
|---|---|
| Evidence completeness | Does a retrieved unit include the facts needed to answer, along with any necessary surrounding context? |
| Retrieval precision | How much irrelevant text accompanies the evidence? |
| Structural fidelity | Are headings, tables, lists, and meaningful Thai spacing preserved? |
| Query and language fit | Does the method work for Thai, English, and code-switched queries in the target corpus? |
| Index and query cost | What additional parsing, embedding, model calls, storage, or retrieval work does it require? |
| Update behavior | Can changed source documents be reprocessed without leaving stale evidence in the index? |
The 2025 paper also introduces PIC, which uses document summaries as pseudo-instructions and groups sentences by semantic similarity to a summary. Its abstract reports improvements on multiple open-domain QA benchmarks in Hits@k and exact match without additional training. This is a reported result for the paper’s evaluated benchmarks, not a general guarantee or evidence of a Thai-specific advantage.
How to chunk Thai text for RAG
Do not assume Thai uses English-like spaces between every word. The 2021 WangchanBERTa paper reports that its Thai-specific preprocessing preserves spaces because they can mark important chunk and sentence boundaries before subword tokenization. The authors state: “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.” Their report also explores SentencePiece, dictionary-based word-level and syllable-level tokenizers, including PyThaiNLP’s newmm, and another tokenizer on Thai Wikipedia data.
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For a RAG pipeline, the practical implication is to avoid stripping meaningful spacing as generic cleanup and to inspect how parsing and segmentation treat actual Thai documents. The following checks are engineering advice based on that finding; the paper does not report production RAG tests of every case.
- Preserve meaningful spaces and document structure during parsing.
- Inspect chunk boundaries around clauses, headings, lists, tables, and switches between Thai and English or code.
- If a tokenizer or segmenter defines boundaries, record its version and test domain terminology, spelling variation, numerals, and mixed-script terms.
- Evaluate retrieved evidence on real Thai and mixed Thai-English questions, rather than judging segmentation only by how chunks look.
The WangchanBERTa authors report a cleaned and deduplicated training set of 78 GB in their 2021 paper. That figure describes their model pretraining corpus; it is not a recommended RAG corpus size or a chunking result. The available Thai-specific evidence here concerns model preprocessing, not an end-to-end comparison establishing a best Thai RAG segmentation strategy or a percentage performance difference between approaches.
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Evaluate the complete evidence path
Build a test set from representative questions your application should answer. For each question, identify the source evidence needed, including cases where the corpus does not contain an answer. Then inspect each stage rather than relying on a single answer-quality score:
- Evidence retrieval: Did search surface the passages containing the labeled evidence?
- Context sufficiency: Did the assembled context include enough of that evidence, without burying it in irrelevant material?
- Answer support: Is each material claim in the answer supported by the passages provided to the model?
- Unanswerable cases: When the corpus lacks the answer, does the system avoid presenting unsupported material as established fact?
- Language coverage: Are Thai and mixed Thai-English questions represented well enough to reveal segmentation and retrieval issues?
Chunk boundaries by themselves are not a sufficient evaluation target. A 2026 ACL paper by Lu and colleagues, “HiChunk: Evaluating and Enhancing Retrieval Augmented Generation with Hierarchical Chunking,” argues that existing RAG benchmarks can inadequately assess chunking when evidence is sparse. It describes HiCBench, with manually annotated multilevel chunk boundaries, evidence-dense QA pairs, and corresponding evidence sources, and presents hierarchical structuring with Auto-Merge retrieval. This makes evidence-oriented chunking evaluation an active research issue; the benchmark and method should not be assumed to represent every corpus or Thai workload.
Production checklist: make the boundaries dependable
- Source handling: Confirm extraction preserves the content and structure the application needs, and retain links between indexed material and source documents.
- Chunking: Compare candidate strategies on representative questions and inspect both missing context and irrelevant text in retrieved passages.
- Thai processing: Test preserved spacing, segmentation, and mixed-script content on the actual document and query domains.
- Retrieval and filtering: Verify that relevant candidates survive search and filtering and fit within the context selected for generation.
- Answer grounding: Keep provenance available and assess whether answers are supported, including on questions the corpus cannot answer.
- Change management: Define how updated source documents trigger reprocessing and how obsolete indexed content is handled.
- Operational fit: Choose storage and search infrastructure based on the workload’s retrieval strategy, filtering, operations, and cost rather than assuming one database type is mandatory.
Set evaluation thresholds and operational targets for the application you are building. The cited sources do not establish universal score thresholds, a production SLA, a universally best chunk size, or provider-level pricing and controls.
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