Time-Weighted Retriever

📖 Overview

A Time-Weighted Retriever considers both relevance and recency when selecting documents.

Traditional vector retrieval primarily asks:

"Which documents are most semantically similar to this query?"

Time-weighted retrieval adds another question:

"How recent is this information?"

This is especially useful for knowledge bases where newer information should gradually become more important.

Examples include:

News
Product Documentation
Support Tickets
Operational Knowledge
Incident Reports
Engineering Discussions
Policy Updates
Research Notes
Conversation History

The core idea is:

Semantic Relevance
        +
Document Recency
        ↓
Time-Weighted Ranking
        ↓
Final Retrieved Context

---

🎯 Learning Objectives

After completing this chapter, you will be able to:

• Understand time-weighted retrieval
• Understand why recency matters in RAG
• Differentiate relevance ranking from recency-aware ranking
• Understand exponential time decay
• Configure decay parameters
• Combine semantic similarity with recency
• Implement time-weighted retrieval
• Understand the role of timestamps and metadata
• Combine time weighting with vector retrieval
• Combine time weighting with other retrieval strategies
• Understand common failure modes
• Design production-ready time-aware retrieval systems
• Evaluate retrieval quality across different time windows

---

1. Why Recency Matters

Consider an enterprise API policy.

API Security Policy

2022 Version
→ OAuth 2.0
→ Token expiration: 24 hours

2024 Version
→ OAuth 2.0
→ Token expiration: 12 hours

2026 Version
→ OAuth 2.0
→ Token expiration: 1 hour

A semantic retriever may find all three documents because they discuss the same topic.

However, if the user asks:

"What is the current token expiration policy?"

the latest version should normally receive higher priority.

Therefore:

Semantic Similarity
        ↓
Find relevant documents

Recency
        ↓
Prefer newer information

---

2. Traditional Retrieval

A conventional vector retriever may rank documents based primarily on semantic similarity.

Query
 ↓
Embedding
 ↓
Vector Search
 ↓
Similarity Score
 ↓
Top-K

Example:

Document             Similarity

Policy 2022            0.94
Policy 2024            0.92
Policy 2026            0.89

A traditional retriever might return:

2022
2024
2026

even though the 2026 document is the most current.

---

3. Time-Weighted Retrieval

Time-weighted retrieval introduces a temporal signal.

Conceptually:

Final Score
=
Relevance
+
Recency Contribution

or, depending on the implementation:

Final Score
=
Similarity × Recency Weight

The exact scoring model depends on the implementation.

The architecture becomes:

flowchart LR
    A["User Query"] --> B["Vector Retriever"]
    B --> C["Candidate Documents"]

    C --> D["Similarity Score"]
    C --> E["Document Timestamp"]

    D --> F["Time-Weighted Ranking"]
    E --> F

    F --> G["Final Ranking"]
    G --> H["Top-K Documents"]

---

4. Core Concept

A time-weighted retriever uses:

Relevance
    +
Age / Recency

to determine the final ranking.

For example:

Document A
Similarity = High
Age = 4 years

Document B
Similarity = Slightly Lower
Age = 2 months

A time-aware system may prefer:

Document B

if recency is sufficiently important.

---

5. Time Decay

The influence of a document can gradually decrease as it becomes older.

A common conceptual model is exponential decay:

Recency Weight = e^(-λt)

where:

t = age of document
λ = decay rate

The behavior is:

New Document
     ↓
High Recency Weight

Older Document
     ↓
Lower Recency Weight

The decay should be selected according to the domain.

---

6. Decay Visualization

Recency Weight
     ↑
1.0  ●
     │\
     │ \
0.8  │  ●
     │    \
0.6  │      ●
     │        \
0.4  │          ●
     │             \
0.2  │                ●
     │                   \
0.0  └────────────────────────→
       New              Older

The exact curve depends on the decay configuration.

The important idea is:

Older documents gradually lose temporal preference.

---

7. Half-Life Concept

A useful way to reason about decay is half-life.

Suppose the recency weight has a half-life of:

30 days

Then approximately:

Age             Recency Weight

0 days             1.00
30 days            0.50
60 days            0.25
90 days            0.125

This does not necessarily mean the document becomes unusable.

It means its recency contribution decreases over time.

---

8. Why Half-Life Matters

Different domains require different temporal behavior.

Breaking News

Half-life:
Hours

Incident Management

Half-life:
Days

Product Documentation

Half-life:
Months

Enterprise Policies

Half-life:
Months / Years

Historical Research

Recency:
May have little or no importance

Therefore:

Time decay must be domain-specific.

---

9. Time-Weighted Retrieval Architecture

flowchart TD
    A["User Query"] --> B["Semantic Retriever"]

    B --> C["Candidate Documents"]

    C --> D["Similarity Signal"]
    C --> E["Created / Updated Timestamp"]

    E --> F["Age Calculation"]
    F --> G["Recency Function"]

    D --> H["Score Combination"]
    G --> H

    H --> I["Final Ranking"]
    I --> J["Top-K Results"]
    J --> K["LLM"]

The retrieval system therefore adds a temporal ranking stage after candidate generation.

---

10. Timestamp Metadata

Time-weighted retrieval depends on reliable timestamps.

A document might contain:

{
  "source": "api-security-policy.pdf",
  "created_at": "2024-05-12T10:30:00Z",
  "updated_at": "2026-07-20T09:15:00Z"
}

Possible temporal fields include:

created_at
updated_at
published_at
effective_from
effective_until
last_modified

The correct field depends on the business meaning of "current."

---

11. Created Time vs Updated Time

These fields are not interchangeable.

Consider:

Document created:
2022

Document updated:
2026

If the system uses:

created_at

the document appears old.

If the business requirement is to prioritize the latest version, then:

updated_at

may be more appropriate.

Therefore:

Temporal Semantics
        ↓
Choose Correct Timestamp

---

12. Effective Dates

Enterprise systems often need more than timestamps.

For example:

{
  "effective_from": "2026-07-01",
  "effective_until": null
}

A policy may have been created earlier but become effective later.

Therefore:

Created Date
    ≠
Effective Date

For policies, contracts, pricing, and regulations, effective dates can be more important than modification dates.

---

13. Validity Windows

Some documents have explicit validity periods.

Example:

Promotion Policy

Effective:
2026-08-01

Expires:
2026-08-31

A production retrieval system can use:

Current Date
      ↓
Validity Check
      ↓
Eligible Documents

before applying semantic retrieval.

This is different from simply applying time decay.

---

14. Time-Weighted Retrieval vs Temporal Filtering

These concepts should be distinguished.

Temporal Filtering

Hard constraint:

Only documents from 2026

Documents outside the range are excluded.

2025 → ❌
2026 → ✅

Time Weighting

Soft preference:

2025 → Lower Score
2026 → Higher Score

Older documents may still be returned if they are highly relevant.

Therefore:

Filtering
→ Hard boundary

Weighting
→ Soft preference

---

15. Combining Filtering and Weighting

A production system can combine both.

Query
 ↓
Temporal Filter
 ↓
Semantic Retrieval
 ↓
Time Weighting
 ↓
Final Ranking

For example:

Filter:
effective_until >= today

then:

Rank active documents
by relevance + recency

This can be safer for time-sensitive enterprise information.

---

16. LangChain Example

LangChain provides a time-weighted retriever abstraction.

A simplified example is:

from langchain.retrievers import TimeWeightedVectorStoreRetriever

retriever = TimeWeightedVectorStoreRetriever(
    vectorstore=vector_store,
    decay_rate=0.01,
    k=5
)

Documents need temporal metadata so that their age can be considered.

For example:

document.metadata = {
    "source": "engineering-notes.md",
    "created_at": "2026-08-01T10:00:00Z"
}

The exact metadata requirements depend on the framework version and implementation being used.

---

17. Adding Documents with Timestamps

Example:

from datetime import datetime, timezone

document.metadata["created_at"] = (
    datetime.now(timezone.utc).isoformat()
)

vector_store.add_documents(
    [document]
)

The retrieval layer can then use temporal information during ranking.

For production systems, timestamps should preferably be assigned from authoritative ingestion metadata rather than generated arbitrarily during retrieval.

---

18. Basic Retrieval Example

results = retriever.invoke(
    "What is the current deployment policy?"
)

for document in results:
    print(document.page_content)
    print(document.metadata)

The result can contain:

Document Content
Source
Timestamp
Metadata

This allows downstream components to understand why a particular version was retrieved.

---

19. Relevance + Recency

Consider three documents:

Document A
Similarity = 0.95
Age = 3 years

Document B
Similarity = 0.90
Age = 6 months

Document C
Similarity = 0.85
Age = 1 week

A purely semantic retriever may prefer:

A
B
C

A time-aware retriever might prefer:

B
C
A

depending on the decay configuration.

This illustrates the central trade-off:

Semantic Relevance
        ↕
Temporal Freshness

---

20. The Freshness-Relevance Trade-Off

A document can be:

Very Relevant
but Old

or:

Very Recent
but Less Relevant

For example:

Old Architecture Guide
Similarity = 0.97

New Release Note
Similarity = 0.83

If the query is:

"What is the latest API behavior?"

the newer document may be more useful.

But if the query is:

"What architecture was used in version 2?"

the older document may be exactly what the user needs.

Therefore, time weighting should never blindly replace relevance.

---

21. Query-Aware Temporal Behavior

Temporal importance can depend on the query.

Compare:

"What is the current pricing?"

with:

"What was the pricing in 2022?"

The first query requires:

High Recency Preference

The second requires:

Historical Relevance

Therefore, advanced systems may adapt temporal behavior based on query intent.

---

22. Query Intent and Recency

Conceptually:

flowchart TD
    A["User Query"] --> B["Query Intent Detection"]

    B --> C["Current / Latest"]
    B --> D["Historical"]
    B --> E["General Knowledge"]
    B --> F["Time-Specific"]

    C --> G["High Recency Weight"]
    D --> H["Low / Targeted Recency"]
    E --> I["Normal Recency"]
    F --> J["Temporal Filter"]

    G --> K["Retrieval"]
    H --> K
    I --> K
    J --> K

This is a more sophisticated approach than applying one decay rate to every query.

---

23. Explicit Temporal Queries

Queries often contain temporal language:

latest
current
recent
today
this month
last year
in 2024
before 2022
after 2025

These terms can be extracted during query processing.

Example:

"What changed in the API in 2026?"

Possible interpretation:

Target Year = 2026

The system can then apply:

Temporal Filter
+
Semantic Retrieval

rather than relying only on generic recency decay.

---

24. Time-Weighted Retrieval with Hybrid Search

Time weighting can be combined with lexical and semantic retrieval.

Query
 ├── Vector Search
 ├── BM25
 └── Temporal Signal
          ↓
       Fusion
          ↓
     Final Ranking

Architecture:

flowchart TD
    A["User Query"] --> B["Vector Retriever"]
    A --> C["BM25 Retriever"]

    B --> D["Semantic Results"]
    C --> E["Lexical Results"]

    D --> F["Candidate Pool"]
    E --> F

    F --> G["Temporal Scoring"]
    G --> H["Final Ranking"]
    H --> I["Top-K"]

This can combine:

Semantic Relevance
+
Keyword Relevance
+
Freshness

---

25. Time-Weighted Retrieval with Ensemble Retrieval

The temporal signal can also be added to an ensemble.

Vector Retriever
BM25 Retriever
Domain Retriever
        ↓
Result Fusion
        ↓
Temporal Ranking
        ↓
Final Results

Alternatively, time-aware retrievers can participate as one of the ensemble components.

The architecture should be selected based on the scoring and evaluation strategy.

---

26. Time-Weighted Retrieval with Re-ranking

A reranker can be applied after temporal retrieval.

Query
 ↓
Time-Weighted Retrieval
 ↓
Candidate Documents
 ↓
Reranker
 ↓
Top-K

Architecture:

flowchart LR
    A["Query"] --> B["Time-Weighted Retriever"]
    B --> C["Candidate Documents"]
    C --> D["Reranker"]
    D --> E["Final Documents"]

This allows:

Retrieval
→ Relevance + Recency

Reranking
→ Deeper Query-Document Relevance

---

27. Time-Weighted Retrieval with Contextual Compression

A useful RAG pipeline is:

Query
 ↓
Time-Weighted Retrieval
 ↓
Candidate Documents
 ↓
Contextual Compression
 ↓
Relevant Context
 ↓
LLM

Architecture:

flowchart TD
    A["Query"] --> B["Time-Weighted Retriever"]
    B --> C["Candidate Documents"]
    C --> D["Contextual Compression"]
    D --> E["Relevant Context"]
    E --> F["Prompt Assembly"]
    F --> G["LLM"]

This is particularly useful when recent documents are large and contain substantial irrelevant information.

---

28. Time-Weighted Retrieval with Multi-Vector Retrieval

Multi-Vector Retrieval can provide multiple representations, while time weighting prioritizes newer documents.

Query
 ↓
Multi-Vector Search
 ↓
Representation Matches
 ↓
Parent Resolution
 ↓
Temporal Ranking
 ↓
Final Documents

Architecture:

flowchart TD
    A["Query"] --> B["Multi-Vector Retriever"]
    B --> C["Representation Matches"]
    C --> D["Parent Resolution"]
    D --> E["Temporal Ranking"]
    E --> F["Final Documents"]

This can be useful when both:

Multiple Semantic Representations
+
Document Freshness

matter.

---

29. Time-Weighted Retrieval with Parent Documents

For document versioning, the architecture can be:

Query
 ↓
Child / Representation Retrieval
 ↓
Parent Documents
 ↓
Version Resolution
 ↓
Time-Aware Ranking
 ↓
Current Context

For example:

Policy v1
Policy v2
Policy v3

The system can prefer:

Policy v3

if it is the latest valid version.

---

30. Version-Aware Retrieval

Versioning is often more reliable than generic recency.

Example:

{
  "document_id": "policy-100",
  "version": "3",
  "effective_from": "2026-07-01",
  "effective_until": null,
  "status": "active"
}

The retrieval pipeline can apply:

Status = active

before ranking.

This prevents an outdated but recently modified draft from outranking the active policy.

---

31. Draft vs Published Documents

Consider:

Policy A
Status = Published
Updated = 2026-08-01

Policy B
Status = Draft
Updated = 2026-08-10

A naive time-weighted system may prefer:

Policy B

because it is newer.

But the enterprise application may need:

Policy A

because it is the current published policy.

Therefore:

Recency is not the same as validity.

This is a critical enterprise retrieval principle.

---

32. Temporal Metadata Model

A robust metadata model can include:

{
  "document_id": "policy-100",
  "version": "3",
  "created_at": "2025-10-01T09:00:00Z",
  "updated_at": "2026-07-15T11:30:00Z",
  "effective_from": "2026-07-01T00:00:00Z",
  "effective_until": null,
  "status": "published"
}

This enables more precise temporal reasoning.

---

33. Temporal Retrieval Pipeline

flowchart TD
    A["User Query"] --> B["Query Understanding"]

    B --> C["Temporal Intent"]
    B --> D["Semantic Intent"]

    C --> E["Temporal Constraints"]
    D --> F["Semantic Retrieval"]

    E --> G["Candidate Filtering"]
    F --> G

    G --> H["Recency / Validity Scoring"]
    H --> I["Reranking"]
    I --> J["Context Selection"]
    J --> K["LLM"]

This architecture separates:

Temporal Constraints

from:

Temporal Preference

---

34. Decay Rate Selection

The decay rate determines how quickly old information loses temporal influence.

Conceptually:

Low Decay
    ↓
Slow freshness decay
    ↓
Older documents remain competitive

While:

High Decay
    ↓
Fast freshness decay
    ↓
New documents strongly preferred

Example:

Slow-changing domain
→ Low decay

Fast-changing domain
→ Higher decay

The correct value should be established through evaluation.

---

35. Decay Configuration

Example configuration:

retrieval:
  time_weighting:
    enabled: true
    decay_rate: 0.01
    timestamp_field: updated_at
    minimum_recency_weight: 0.1

An enterprise implementation may expose:

decay_rate
timestamp_field
minimum_weight
time_window

as configuration rather than hard-coding them.

---

36. Domain-Specific Profiles

Different domains can use different temporal profiles.

Example:

profiles:

  news:
    decay_rate: 0.20

  support:
    decay_rate: 0.05

  engineering:
    decay_rate: 0.01

  policies:
    decay_rate: 0.005

These numbers are illustrative.

The important architectural idea is:

Domain
   ↓
Temporal Profile
   ↓
Retrieval Configuration

---

37. Query-Specific Time Windows

Instead of continuous decay, some applications use explicit windows.

For example:

Current incidents:
Last 7 days

or:

Recent product changes:
Last 90 days

The pipeline becomes:

Query
 ↓
Time Window Extraction
 ↓
Temporal Filter
 ↓
Semantic Retrieval

This is often preferable when the user explicitly specifies a time range.

---

38. Temporal Filtering Example

Suppose:

Query:
"What changed in the API during July 2026?"

The system can derive:

start = 2026-07-01
end   = 2026-07-31

Then:

documents = filter_by_date(
    documents,
    start_date="2026-07-01",
    end_date="2026-07-31"
)

Only after filtering should the system perform deeper ranking.

---

39. Historical Questions

Time weighting can become harmful for historical queries.

Example:

"What authentication mechanism did the platform use in 2021?"

The latest document may describe:

OAuth 2.1

while the 2021 document describes:

OAuth 2.0

If recency dominates:

Current Document
      ↓
Incorrect Historical Answer

Therefore, explicit historical intent should override generic recency preference.

---

40. Temporal Query Classification

A query classifier may identify:

Current
Historical
Time-Specific
Time-Neutral

Example:

"What is the current pricing?"
→ Current

"What was the pricing in 2022?"
→ Historical

"What changed between 2024 and 2026?"
→ Time-Specific

"How does OAuth work?"
→ Time-Neutral

This enables more appropriate retrieval behavior.

---

41. Time-Weighted Retrieval and Citations

Temporal retrieval makes source metadata especially important.

A response should ideally identify:

Document
Version
Effective Date
Source

For example:

Source:
API Security Policy

Version:
3

Effective:
July 1, 2026

This helps users understand why a newer policy was selected.

---

42. Temporal Citations

Example response context:

{
  "content": "Production APIs require OAuth 2.0 access tokens.",
  "source": "api-security-policy.pdf",
  "version": "3",
  "effective_from": "2026-07-01",
  "page": 14
}

This is more useful than:

{
  "content": "Production APIs require OAuth 2.0 access tokens."
}

because the temporal context is preserved.

---

43. Observability

Time-aware retrieval should expose temporal signals.

Example:

Query
 ↓
Candidate Documents

Document A
Similarity: 0.94
Age: 720 days
Recency Weight: 0.18

Document B
Similarity: 0.89
Age: 30 days
Recency Weight: 0.74

Document C
Similarity: 0.86
Age: 7 days
Recency Weight: 0.91

This makes retrieval decisions explainable.

---

44. Retrieval Trace

A production trace could contain:

{
  "query": "What is the current token policy?",
  "retriever": "time_weighted_vector",
  "candidates": 20,
  "timestamp_field": "updated_at",
  "decay_rate": 0.01,
  "top_result": {
    "document_id": "policy-100",
    "similarity": 0.89,
    "recency_weight": 0.92
  }
}

This is useful for:

Debugging
Evaluation
Observability
Auditing

---

45. Evaluation Strategy

A time-weighted retriever should be evaluated against a baseline.

Baseline:
Vector Retriever

Experiment:
Time-Weighted Retriever

Compare:

Recall@K
MRR
NDCG
Answer Accuracy
Temporal Accuracy
Citation Accuracy
Latency

The key additional metric is:

Temporal Correctness

Does the system retrieve the correct version or time period?

---

46. Temporal Evaluation Dataset

Create test cases such as:

Query:
"What is the current API timeout?"

Expected:
2026 Policy

Query:
"What was the API timeout in 2023?"

Expected:
2023 Policy

Query:
"What changed between 2024 and 2026?"

Expected:
Both versions

This helps evaluate whether the system understands temporal intent rather than simply preferring newer documents.

---

47. Example Evaluation Table

Query Type	Vector	Time-Weighted	Temporal Filter
Current	0.78	0.88	0.91
Historical	0.84	0.72	0.90
Time-specific	0.70	0.81	0.93
Time-neutral	0.87	0.86	0.84

The numbers are illustrative.

The important observation is that:

No single temporal strategy
is optimal for every query type.

---

48. Common Failure Modes

48.1 Overweighting Recency

Newer
≠
More Relevant

A recent document can still be unrelated.

---

48.2 Ignoring Historical Intent

A current document may incorrectly replace the historical document required by the user.

---

48.3 Wrong Timestamp

Using:

created_at

when the business requirement is:

effective_from

can produce incorrect results.

---

48.4 Draft Documents

A recently updated draft may outrank a valid published document.

---

48.5 Stale Metadata

Incorrect timestamps lead directly to incorrect temporal ranking.

---

48.6 Excessive Decay

If decay is too aggressive:

Older Knowledge
     ↓
Rapidly Loses Influence

Important long-lived knowledge may disappear.

---

48.7 Insufficient Decay

If decay is too weak:

Very Old Documents
     ↓
Remain Highly Competitive

The system may fail to prioritize current information.

---

49. Production Architecture

A mature enterprise temporal retrieval architecture can look like:

flowchart TD
    A["User Query"] --> B["Query Understanding"]

    B --> C["Semantic Intent"]
    B --> D["Temporal Intent"]

    D --> E["Temporal Constraints"]
    C --> F["Candidate Retrieval"]

    E --> G["Temporal Filtering"]
    F --> G

    G --> H["Candidate Documents"]

    H --> I["Relevance Scoring"]
    H --> J["Recency / Validity Scoring"]

    I --> K["Temporal Ranking"]
    J --> K

    K --> L["Reranker"]
    L --> M["Contextual Compression"]
    M --> N["Prompt Assembly"]
    N --> O["LLM"]

    O --> P["Response Validation"]
    P --> Q["Citation / Version Attribution"]
    Q --> R["Enterprise Response"]

This architecture separates:

Temporal Intent
        ↓
Temporal Constraints
        ↓
Retrieval
        ↓
Recency Preference
        ↓
Precision Optimization
        ↓
Generation

---

50. Framework-Agnostic Interface

An enterprise AI platform can define a temporal retrieval interface:

from abc import ABC, abstractmethod


class TimeAwareRetriever(ABC):

    @abstractmethod
    def retrieve(
        self,
        query: str,
        top_k: int,
        *,
        timestamp_field: str = "updated_at"
    ) -> list:
        pass

Possible implementations:

class VectorTimeWeightedRetriever(TimeAwareRetriever):
    ...


class HybridTimeWeightedRetriever(TimeAwareRetriever):
    ...


class VersionAwareRetriever(TimeAwareRetriever):
    ...

This allows the application to remain independent of the underlying retrieval framework.

---

51. Configuration-Driven Architecture

A production configuration might look like:

retrieval:
  temporal:
    enabled: true

    timestamp_field: updated_at

    decay:
      strategy: exponential
      rate: 0.01

    validity:
      enabled: true

    versioning:
      enabled: true

    historical_queries:
      disable_recency_bias: true

This provides explicit control over temporal behavior.

---

52. Decision Framework

flowchart TD
    A["User Query"] --> B{"Contains Temporal Intent?"}

    B -->|Yes| C{"Historical or Current?"}

    C -->|Historical| D["Apply Time Constraint"]
    C -->|Current| E["Prefer Recent Valid Documents"]

    B -->|No| F["Use Normal Retrieval"]

    E --> G["Semantic + Temporal Ranking"]
    D --> H["Temporal Filtering + Retrieval"]

    F --> I["Semantic Retrieval"]

    G --> J["Reranking"]
    H --> J
    I --> J

    J --> K["Context Selection"]

This avoids blindly applying recency to every query.

---

53. When to Use Time-Weighted Retrieval

It is particularly useful when:

• Information changes frequently
• Newer documents are generally more useful
• Knowledge bases contain multiple document versions
• Users frequently ask for current information
• Support knowledge evolves over time
• Engineering documentation changes frequently
• Operational information becomes stale
• Conversation or interaction history should favor recent information

Examples:

Support Knowledge
Incident Management
Product Releases
Engineering Discussions
News
Operational Runbooks
Current Policies

---

54. When It May Not Be Appropriate

Time weighting may be less useful when:

Knowledge is stable

or:

Historical information is equally valuable

or:

The query explicitly targets an older time period

or:

Document validity is determined by status/version
rather than modification time

For example:

Historical Research
Legal Archives
Academic Literature
Historical Policies

may require temporal filtering or explicit time targeting rather than generic recency decay.

---

55. Recommended Enterprise Pattern

A robust enterprise pattern is:

Query
 ↓
Query Intent Detection
 ↓
Temporal Intent Detection
 ↓
Validity Filtering
 ↓
Semantic / Hybrid Retrieval
 ↓
Recency-Aware Ranking
 ↓
Reranking
 ↓
Contextual Compression
 ↓
Citation + Version Attribution
 ↓
LLM

Architecture:

flowchart LR
    A["Query"] --> B["Intent Detection"]
    B --> C["Temporal Constraints"]
    C --> D["Validity Filtering"]
    D --> E["Retrieval"]
    E --> F["Time-Aware Ranking"]
    F --> G["Reranking"]
    G --> H["Context Selection"]
    H --> I["LLM"]
    I --> J["Citation / Version"]

This is safer than simply applying a decay function to every retrieval request.

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56. Production Checklist

Before deploying time-weighted retrieval:

☐ Temporal requirements are clearly defined
☐ Correct timestamp field is selected
☐ Effective dates are considered where applicable
☐ Document validity is represented
☐ Published vs draft status is represented
☐ Version information is preserved
☐ Decay configuration is externalized
☐ Decay parameters are evaluated
☐ Historical queries are handled separately
☐ Explicit temporal filters are supported
☐ Source metadata is preserved
☐ Temporal signals are observable
☐ Retrieval baseline is available
☐ Temporal correctness is evaluated
☐ Latency impact is measured
☐ Cost impact is measured
☐ Regression tests cover current and historical queries
☐ Citation and version attribution are preserved

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57. Key Takeaways

• Time-Weighted Retrieval combines relevance with temporal information.
• It is useful when newer information should receive greater preference.
• Recency should generally be treated as a ranking signal rather than an absolute replacement for relevance.
• Exponential decay is one common conceptual model for reducing the influence of older documents.
• Decay parameters should be selected according to the domain.
• Created, updated, published, and effective timestamps have different meanings.
• Recency is not the same as validity.
• A recently updated draft should not automatically outrank a current published policy.
• Temporal filtering and temporal weighting solve different problems.
• Explicit historical queries should not be blindly biased toward recent documents.
• Query intent can determine how strongly recency should influence retrieval.
• Time-aware retrieval can be combined with vector, BM25, ensemble, multi-vector, reranking, and contextual compression techniques.
• Temporal metadata should remain available for citations and auditing.
• Production systems should evaluate both retrieval quality and temporal correctness.
• The objective is not simply to retrieve the newest information.
• The objective is to retrieve the most relevant information for the requested time context.

The central pattern is:

Understand Time Intent
        ↓
Apply Validity Constraints
        ↓
Retrieve Relevant Candidates
        ↓
Apply Temporal Preference
        ↓
Rank
        ↓
Generate from Correct Evidence

Or simply:

Relevant
    +
Temporally Correct
    ↓
Better Enterprise Retrieval

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🧭 Chapter Navigation

Part V — Advanced Retrieval-Augmented Generation

Previous:
03. Multi-Vector Retriever

Next:
05. Hybrid Search Retriever

Section:
02 — Enterprise Retrieval Engineering

Enterprise Retrieval Engineering Path

01 Contextual Compression Retriever
              ↓
02 Ensemble Retriever
              ↓
03 Multi-Vector Retriever
              ↓
04 Time-Weighted Retriever
              ↓
05 Hybrid Search Retriever
              ↓
06 HyDE Retriever
              ↓
07 Router Retriever
              ↓
08 Multi-Stage Retrieval
              ↓
09 Agentic Retrieval
              ↓
10 Re-ranking Techniques
              ↓
11 MMR & Diversity-Aware Retrieval
              ↓
12 Metadata-Aware Retrieval
              ↓
13 Advanced Query Rewriting

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Enterprise AI Engineering Handbook
Building Production-Grade Enterprise AI Systems — One Chapter at a Time.