Understand first
New to this knowledge point
Complete the prerequisite concepts, read the principles and counterexamples, and then explain why in your own words.
Start with core principles →Understand → Implement → Debug → Design
Examine document structure, search units, contextual completion, and reviewable chunking experiments.
Knowledge content check2026-10-03 · Check the source of the original question2026-10-02
It is recommended to understand first:
Prompts, structured output, and iterative acceptance checks →Context and generation budgets →Tool calls: structure, authorization, and business contracts →Select the starting point based on the current basis, or you can go deeper one by one. When you encounter an unfamiliar concept, go back to the core principles first; use the knowledge exercises to check your understanding when you are finished.
New to this knowledge point
Complete the prerequisite concepts, read the principles and counterexamples, and then explain why in your own words.
Start with core principles →Prepare to write the principles into code
Understand implementation steps and boundaries, complete small tasks, and check results against acceptance requirements.
Reading implementation and trade-offs →Need to handle failures and changes in conditions
Follow the continuous questioning to locate the failure premise, and then compare the migration cases to explain how the plan should be adjusted.
Continue to delve deeper into the problem →Need to design or review plans
Combine engineering deductions and senior self-evaluation standards to explain the applicable conditions, costs and alternatives of the plan.
Analyze engineering scenarios →LEARN · PRACTICE · REFLECT
First read along the principles, Q&A and migration cases. When you need to check your understanding, switch to reinforcement exercises or start personal recording.
Can be practiced directly. After logging in, answers, favorites, and notes will be saved to your account.
Log in and saveEach modified commit will be kept as an independent history. Your level of mastery is up to you to evaluate yourself against the standards.
Core concept · Semantic completeness and evidence location in chunking
Preparatory concepts:Document parsing, Token budget, Source and permission metadata
Chunking trades retrieval granularity against evidence completeness. Each chunk must preserve the conditions and relationships needed to understand it. High vector similarity cannot restore missing relationships.
Fixed-length chunks offer a cheap baseline but can separate rules from exceptions or values from units. “500” alone may denote yuan, ten thousand yuan, milliseconds, or a version. Chunking should produce interpretable, citable evidence rather than evenly sized text.
Preserve clause hierarchy and definitions in contracts, headers and units in tables, and symbols, versions, dependencies, and call boundaries in code. Parsing errors precede chunking; overlap cannot restore a missing header. Retain pages, ranges, and versions for source verification.
Small chunks target local questions; parent or neighboring sections add context. Expansion from a public paragraph must not expose private attachments. Repeated expansion of one parent can also waste tokens. Check authorization and deduplicate expanded evidence.
Compare strategies on main rules, exceptions, table relationships, and cross-function behavior. Measure candidate coverage, complete evidence, and citation accuracy. Overlap costs index space and duplicate candidates without guaranteeing preserved relationships. Choose for the task distribution, with separate handling for unusual structures.
Choose chunks around questions and document structure. Preserve contract hierarchy and exceptions, table headers and units, and code symbols and dependencies. Retrieve small chunks and expand to parent or neighboring context within access and token budgets, controlling duplicates. Use fixed length as a baseline, then evaluate annotated evidence coverage, answer completeness, and citation positions.
Asking "What are the liquidated damages?" requires the amount, triggering conditions and exceptions; asking about the trend of the table requires the header, unit and time columns. Splitting solely by character count can separate negations or units from their statements. Each block retains at least document_id, version, chapter path, page number or line range, and the numbers in the extracted text can be traced back to the source for verification. Parsing failures must be visible, and empty text cannot be stored as a success.
First use title, paragraph, table and function boundaries to establish candidate blocks, and then recursively split them when the budget is exceeded. When the table is divided into batches, the necessary headers are repeated and indicate whether there are continuation rows; the code retains function signatures, file paths and related type references. Overlap is only used to reduce boundary loss. Excessive overlap will squeeze multiple approximate blocks into the top few, reducing evidence diversity.
Exact chunks are suitable for matching questions where the answer may require context and parent segments or adjacency chunks can be read after recall. Extensions must be made under the same document version and current permissions, and the budget calculated again. Just because the parent document is visible does not mean that all associated documents are visible by default. Answer citations should still be directed to specific passages that support the conclusion, not just a link to the entire manual.
Prepare a set of questions including cross-segment exceptions, table units, and code call relationships, and mark the necessary evidence for each question. Hold retrieval and generation configurations fixed, change only the chunking strategy, and compare necessary evidence coverage, final answer correctness, repetition rate, and cost. Larger chunks may improve recall but reduce answer focus, so average similarity cannot be used to select the best solution.
Continue reading along with the premises and constraints of the problem. Understand the reference answers first, then try to put away the answers and explain the cause and effect and trade-offs in your own words.
Level 1How to retain the table header when the table spans two pages?
Implement structural integrity to tables that are most likely to lose context.
First, continuous tables are identified in the parsing stage, and cross-page table headers and column mappings are saved as structured information; table names, column names, units, and original page coordinates are attached to each row block. Repeated headers are not business data and should not be counted as extra rows. If it cannot be reliably merged, keep two pages and mark them for verification. Do not let the model guess the correspondence between the columns visually.
Follow this answer further
Level 2If the column order changes after a two-page spread, can I continue to reuse the header on the first page?
After the parent asks to retain the table header, it further changes the column structure and checks whether the inheritance is still established.
Table names cannot be reused just because they have the same name. Compare column names, units and positions, process merged cells and new columns, and establish page-level column mapping. Only after the relationship is determined can it be spliced; otherwise, the original page structure will be retained and manual verification or the use of a more reliable parser will be required. Faulty automatic splicing can produce data that appears neat but is semantically misaligned.
Follow this answer further
Level 3OCR has mixed the two columns into one segment. Can subsequent chunking still remedy the problem?
When the structural mapping is agnostic, the problem shifts from dicing to one where the source of the information is corrupted and one must go back to the original material.
chunking cannot reliably restore relationships that have been lost. Return to the original image or PDF page to re-analyze and check the coordinates, rows, and units; if it cannot be verified, mark the material as unusable for accurate calculations. The model can propose candidate explanations to help check, and guessed tables cannot be regarded as verified evidence.
Level 1Is greater overlap necessarily better?
Examining the costs and boundaries of a common repair method.
Not necessarily. Overlap can mitigate sentence or code truncation, but too much overlap can create nearly identical candidates, increase overhead, and crowd out context. Identify relationships that need to cross boundaries before retaining them with smaller overlaps or structured parent-child references. When evaluating, look at the coverage of necessary evidence and the proportion of repetitions, not just the increase in similarity.
Level 1Can parent-chunk expansion bypass authorization?
How to choose between complete context requirements and data permissions when they conflict.
Yes, if the system incorrectly treats the child block's permissions as the entire parent block's permissions. Verify the actual ACL of the parent block and added paragraphs before expanding, and only send the currently readable portion. If the answer cannot be answered due to the lack of complete conditions, it should be explained that the evidence is incomplete and the authorization cannot be relaxed in order to maintain semantics.
First find out the conditions for change, and then determine which premises in the original plan still hold true. The following cases are teaching deductions to facilitate the transfer of principles to new problems.
Changing conditions:The evidence involves functions and external calls
Extended question:The function exceeds the length budget, is it enough to cut it into several pieces?
Preserve symbols, signatures, control flow fragments, and file versions first, then create navigable references to dependencies. When the user asks for timeout processing, read the relevant exception branches and called functions as needed; do not insert the entire repository for the sake of completeness of a single block. Code explanations are required to indicate which behavior depends on external configuration, and necessary evidence is retrieved individually across files.
The principles that remain unchanged:To preserve the semantic relationships required by the problem, text length cannot be substituted for behavioral boundaries.
Changing conditions:Answer relies on non-adjacent paragraphs
Extended question:Can only expanding adjacent blocks avoid missing disclaimers?
No. Build links by clause reference and definition relationships, and when recalling a main clause check the appendices, exceptions and applicable versions it references. Leave evidence gaps for appendices that are necessary but do not have the right to read, and refuse to give a complete conclusion. Such non-adjacent evidence problems are added to the annotation set to verify the expansion strategy.
The principles that remain unchanged:Contextual integrity is determined by dependencies, physical proximity is just one clue.
It is designed based on public technical information; the reference materials support the technical mechanism, and the scenarios and scoring standards are designed by this website and do not represent the original interview questions of a certain company. New Q&A and migration cases are added for principle explanation, and source verification and case operation verification are recorded separately.
After reading, you can explain the principles, boundaries, and trade-offs against these standards. It is up to you to evaluate your mastery; if further verification is needed, complete the small tasks below.
Transferring pre-retrieval filtered ideas to memory versions and recovery. Observe how existing drafts become invalid after cancellation.
Read full text and fault analysis → · Download Reliability Experiment v3 ↓
python3 cli.py memory-put --db memory.sqlite
python3 cli.py submit --db memory.sqlite
python3 cli.py run --db memory.sqlite --lease-seconds 2 --fault after_draft
python3 cli.py memory-forget --db memory.sqlite
# 等待至少 2 秒后分别执行
python3 cli.py run --db memory.sqlite
python3 cli.py inspect --db memory.sqliteVerify local scope, version, and undo blocking; old checkpoints remain, no complete deletion of logs, backups, or checkpoints is provided.
Design block structure and reference fields for a document containing main clauses, exceptions, and tables.