RAG for Debugging AI: Turning Logs, Runbooks, and Incidents into Context-Aware Fixes

A practical blueprint for building a retrieval-augmented debugging AI: ingest code, traces, runbooks, and postmortems; choose embeddings and indexes; ensure freshness, governance, and privacy to cut MTTR.

TL;DR

• Debugging AI systems is a retrieval problem. Answers exist across logs, traces, code, runbooks, and postmortems—but they’re siloed and hard to correlate under pressure.
• A purpose-built Retrieval-Augmented Generation (RAG) stack can cut mean time to resolution (MTTR) by turning operational exhaust into context-aware fixes.
• The core blueprint: build domain-specific indexes (code, logs/traces, runbooks/postmortems), use hybrid search plus rerankers, enforce freshness with streaming ingestion, and gate everything behind governance, ACLs, and PII redaction.
• Focus on measurable outcomes: retrieval recall@k, answer groundedness, time-to-first-signal, and MTTR reduction.

Why RAG for Debugging AI?

Production AI is a distributed system with moving parts: models, feature stores, agents and tools, vector DBs, orchestration, data pipelines, and downstream services. When things fail, you need:

• The exact symptom (error bursts, latency spikes, drift alerts) from logs and traces.
• The most relevant fix (known issues, runbooks, SRE tips) from wiki and incident retros.
• The causal context (recent deploy, prompt change, embedding backfill, feature drift) from code and change history.

LLMs excel at synthesis but struggle without precise context. RAG—if done right—grounds LLM answers in the best available knowledge and makes them auditable with citations.

The twist: debugging data is temporal, multi-modal, and access-controlled. That changes embedding choices, indexing strategies, and governance requirements. The remainder of this article is a step-by-step recipe to build a pragmatic, production-grade RAG stack for debugging AI.

Architecture Overview

User (SRE/On-call)            PagerDuty/Jira Agent
        |                                |
        v                                v
    Query Router  <---- Telemetry ----  Signals (alerts, incidents)
        |
        v
  Query Understanding (rewrite, task-type detection, time scope)
        |
        +--------------+------------------+-------------------+
                       |                  |                   |
                      v                  v                   v
            Code Index (AST, git)   Logs/Trace Index   Runbooks/Postmortems
           (dense+lexical+graph)     (temporal+hybrid)     (dense+BM25)
                       \                |                  /
                        \               |                 /
                         v              v                v
                           Candidate Merge + Reranker (bge/cohere/colbert)
                                                 |
                                                 v
                                       Context Builder (temporal + ACL)
                                                 |
                                                 v
                                         LLM Answer + Citations
                                                 |
                                                 v
                                         Summaries/Actions/Links

Key design decisions:

• Maintain separate domain-specific indexes and merge late. Code and logs behave differently; don’t force one embedding to do both.
• Bias retrieval by time for logs/traces; by stability for runbooks/postmortems; by scope (file/module) for code.
• Rerank across sources with a strong cross-encoder or ColBERT-style late interaction when latency allows.
• Enforce governance and PII redaction at ingestion and at query-time.

Data Ingestion: What to Index and How

You will ingest four primary modalities. Each benefits from different chunking, metadata, and embeddings.

1) Code and Configuration

• Sources: Git repos, IaC (Terraform, Helm), pipeline configs, prompt templates, orchestration (Airflow, Argo), feature store schemas.
• Chunking:
  • Function/class-level chunks for code (preserve AST boundaries; include docstrings and tests).
  • Config files by logical blocks (e.g., a Helm values key).
  • Include inbound/outbound symbol references to navigate across files.
• Metadata:
  • repo, branch, commit, path, language, symbol names, owning team, service.
• Embeddings:
  • Prefer code-aware embeddings for code chunks: e.g., text-embedding-3-large (general), nomic-embed-text-v1.5 (good all-rounder), or open-source CodeBERT/UniXcoder for local.
  • Use lexical BM25 in parallel for exact identifiers and error strings.

Opinion: Hybrid (BM25 + dense) is non-negotiable for code. Identifiers, error codes, and config keys often require exact string matching.

2) Logs

• Sources: Application logs, model inference logs, agent tool logs, vector DB logs, gateway/proxy logs.
• Chunking:
  • Sliding windows of 20–100 lines, respecting request/session/trace_id boundaries.
  • Include structured fields as JSON; carry parsed key-value pairs (status_code, error_class, model, route, datacenter).
• Metadata:
  • time range, environment, service, region, severity, trace_id/span_id, request_id, deployment hash.
• Embeddings:
  • Text-oriented embeddings work; logs are messy but semantic signals (error class, stack trace) benefit from dense vectors.
  • Keep a robust lexical index because exact substrings (e.g., KeyError: uid) are critical.
• Temporal:
  • Apply recency weighting (time-decay) at retrieval. Most incidents hinge on the last deploy or the current window.

3) Traces (OpenTelemetry)

• Sources: OTel spans, service graphs, span events, resource attributes.
• Chunking:
  • Per-trace summaries plus span-level snippets with attributes and errors.
  • Auto-summarize long traces into bottleneck narratives.
• Metadata:
  • trace_id, span_id, parent, service, operation, latency, error flag, release.
• Embeddings:
  • Summaries (text) for dense retrieval; structured filters (service:xyz AND error:true) for pre-filtering.

4) Runbooks and Postmortems

• Sources: Wiki/Confluence/Notion pages, markdown, ADRs, incident retrospectives, Slack threads summarized.
• Chunking:
  • Headings and sections; keep procedures and prerequisites together.
  • Extract checklists and remediation steps as structured JSON in parallel.
• Metadata:
  • owning team, service scope, last updated, severity level addressed, tags (throttling, quota, billing, cache, retry).
• Embeddings:
  • General-purpose text embeddings (E5-large-v2, bge-large-en-v1.5, OpenAI text-embedding-3-large).

Index Design: Hybrid First, Rerank Second

• Use a hybrid retrieval layer: BM25 or BM25L for exact terms and dense ANN (HNSW) for semantics.
• Consider multi-index fanout: query all relevant indexes (code, logs/traces, runbooks) then merge candidates.
• Rerank top 100–200 candidates with a cross-encoder (Cohere Rerank-3, bge-reranker-v2-m3) or ColBERTv2 for late interaction. Reranking improves precision dramatically for operational questions.
• Partition indexes by environment (prod/staging), team, and data sensitivity for fast metadata filtering.
• For vector DBs, HNSW dominates for low-latency. Qdrant, Milvus, Weaviate, Pinecone, and Vespa are strong choices; FAISS/HNSWlib are good embedded options.

Parameter tips:

• HNSW: M ~ 16–64, efConstruction ~ 200–400, efSearch tuned per latency SLO; use cosine for normalized embeddings.
• Sharding: shard by time (logs), repo/service (code), and team (runbooks). Keep shards small enough to rebalance.
• Inverted index: Elastic/OpenSearch with BM25 and kNN plugin works well for hybrid; or use Vespa for native hybrid.

Embedding Model Choices that Actually Matter

• Text (runbooks/postmortems): E5-large-v2, bge-large-en-v1.5, OpenAI text-embedding-3-large, or Voyage-large-2. Choose one validated on MTEB.
• Logs (noisy, domain-specific): bge-small-en-v1.5 is fast and strong; for hosted, OpenAI text-embedding-3-small is cost-effective.
• Code: OpenAI text-embedding-3-large performs well across code/text; local options include CodeBERT or StarEncoder, but expect lower recall without reranking.
• Rerankers: bge-reranker-v2-m3 (open), Cohere Rerank-3 (hosted), or ColBERTv2 (late-interaction) for better long-context precision.

Avoid one-size-fits-all embeddings. Keep separate spaces for code vs text vs logs. Merge with late reranking.

References to benchmark: BEIR and MTEB (Muennighoff et al.) are solid signals for text; they won’t capture code/log quirks—your own eval set is essential.

Chunking and Metadata That Save Incidents

• Respect natural boundaries: AST nodes for code; session/trace windows for logs; headings for runbooks.
• Include “why this matters” metadata: commit hash, owner, release version, deploy job ID, feature flag state.
• Threading: For logs/traces, thread by trace_id and include previous/next windows in metadata to enable expansion.
• Summaries: Precompute TL;DR for heavy traces and long postmortems; store both original and summary—route queries differently.

Freshness: Your RAG is Only as Current as Its Index

• Adopt streaming ingestion for logs/traces via Kafka/NATS and a micro-batcher that computes embeddings within seconds. Use eventual consistency but keep end-to-end SLA under 30–60 seconds.
• Code and runbooks: trigger re-index on git push and wiki page updates. Deduplicate with content hashing; only re-embed changed chunks.
• TTL policies: Logs age out quickly; keep 3–14 days dense index, with cold archival in object storage and lexical-only longer.
• Cache invalidation: On incident creation or deploy, prefetch and pin top shards and index segments relevant to the changed services.
• Time-aware retrieval: Multiply dense/lex scores by a time-decay factor for logs; allow user override (e.g., “search last 24h”).

Governance, Privacy, and Safety for On-Call Reality

• Row-level security: Enforce ABAC/RBAC at query-time and result-time. Never serialize restricted snippets into the LLM context.
• PII/secrets: Redact at ingestion with DLP/Presidio; detect keys/tokens with entropy rules and known patterns; store a reversible tokenization map for authorized users only.
• Multi-tenancy: Partition indexes by tenant/org/project; attach signed filters to requests. Don’t rely on the client to provide correct filters.
• Prompt-injection from logs: Logs are untrusted. Strip control-like patterns and restrict system messages to a fixed policy. Use a content firewall to neutralize “ignore previous instructions”-style strings appearing in logs.
• Data residency: Keep indices in-region; block cross-region retrieval for tagged docs.

Query Understanding and Orchestration

• Classify intent: Is the user asking for root cause, a fix, or code location? Use a lightweight classifier or rules on keywords (e.g., “stacktrace”, “OOM”, “roll back”).
• Query rewrite:
  • Expand with service names, deployment hash, and recent incident IDs.
  • Convert vague “5xx spike after deploy” into structured filters: env=prod, service=api, time=-2h..now, error_class=5xx.
• Multi-hop retrieval:
  • Hop 1: get the exact symptom from logs/traces.
  • Hop 2: retrieve known issues and runbooks for matched patterns.
  • Hop 3: fetch code/config segments that implement the broken path.
• Context windows: Keep context under hard caps and prefer many short, high-precision chunks to a few long ones. Rerank aggressively.

Prompting for Debugging: Make Answers Auditable

Use prompts that force citations, actions, and uncertainty reporting.

Example system prompt for “fix suggestion with provenance”:

text
You are a senior SRE assisting with an ongoing incident. Answer using only the provided CONTEXT. If missing information is required, state it explicitly. 
Output JSON with fields: summary, likely_cause, fix_steps[], references[] (with ids and scores), confidence (0-1).
Do not include any information not grounded in CONTEXT.

Example user content:

text
GOAL: Explain and fix the latency spike in service=ranking after the last deploy.
CONSTRAINTS: env=prod, time window=last 90 minutes, release=2026-01-17. 
CONTEXT:
[1] logs#A12: 2026-01-17T12:07Z ... timeout connecting to feature-store (p50=180ms->900ms) release=...
[2] trace#C51: span feature-store.getFeature latency=920ms; error=true; region=us-east-1
[3] runbook#RS-42: Known issue: feature store throttling after schema migration; mitigation: raise read concurrency to 64 and warm cache.
[4] code#ranking_service.py:L112-L168: synchronous fetch_features(); TODO: add circuit breaker backoff

Retrieval and Answer Quality: What to Measure

• Retrieval: recall@k, nDCG@k on your in-domain questions. Don’t guess—build an eval set.
• Answer: groundedness (are citations sufficient?), hallucination rate, exactness of steps, reproducibility.
• Latency: Time-to-first-candidate, time-to-answer, and p95 under incident load.
• Operational: MTTR, time-to-first-signal, deflection of L3 escalations, and rate of repeat incidents with “known issue” tags.

Create a golden dataset: 50–200 past incidents with questions, correct snippets, and expected actions. Re-run on every change to embeddings, chunking, or indexes.

A Concrete Pipeline: From Telemetry to Answers

Below is an end-to-end reference using Python, OpenTelemetry for traces, Kafka for streaming, Qdrant for vectors, and a reranker. Swap components as needed.

Ingestion and Indexing

python
# requirements: qdrant-client, sentence-transformers, kafka-python, opentelemetry-sdk, uvloop
import asyncio
import json
import hashlib
from datetime import datetime, timezone
from qdrant_client import QdrantClient
from qdrant_client.http.models import Distance, VectorParams, PointStruct
from sentence_transformers import SentenceTransformer
from kafka import KafkaConsumer

INDEX_LOGS = "rag_logs"
INDEX_RUNBOOKS = "rag_runbooks"
INDEX_CODE = "rag_code"

client = QdrantClient(host="localhost", port=6333)

# Create collections if not exist
for name in [INDEX_LOGS, INDEX_RUNBOOKS, INDEX_CODE]:
    try:
        client.get_collection(name)
    except Exception:
        client.recreate_collection(
            collection_name=name,
            vectors_config=VectorParams(size=768, distance=Distance.COSINE)
        )

# Choose a fast, solid embedding model for logs/runbooks (you can use different ones per index)
embedder = SentenceTransformer("BAAI/bge-small-en-v1.5")

def chunk_log_record(record: dict) -> str:
    # Flatten structured fields
    keys = [f"{k}={record.get(k)}" for k in ["service", "env", "severity", "trace_id", "release"] if k in record]
    return f"{record['ts']} {record.get('message','')}\n" + " ".join(keys)


def upsert_points(index: str, docs: list[dict]):
    if not docs:
        return
    payloads = []
    texts = []
    ids = []
    for d in docs:
        text = d["text"]
        texts.append(text)
        payloads.append(d["meta"]) 
        # deterministic id for dedup
        ids.append(int(hashlib.md5(text.encode()).hexdigest()[:16], 16))
    vectors = embedder.encode(texts, normalize_embeddings=True)
    points = [PointStruct(id=ids[i], vector=vectors[i].tolist(), payload=payloads[i]) for i in range(len(texts))]
    client.upsert(collection_name=index, points=points)

# Kafka consumer for logs
consumer = KafkaConsumer("logs", bootstrap_servers=["localhost:9092"], group_id="rag-ingest")

batch = []
BATCH_SIZE = 128

for msg in consumer:
    record = json.loads(msg.value)
    text = chunk_log_record(record)
    meta = {
        "type": "log",
        "service": record.get("service"),
        "env": record.get("env"),
        "severity": record.get("severity"),
        "ts": record.get("ts"),
        "trace_id": record.get("trace_id"),
        "release": record.get("release"),
        "ttl_days": 14,
    }
    batch.append({"text": text, "meta": meta})
    if len(batch) >= BATCH_SIZE:
        upsert_points(INDEX_LOGS, batch)
        batch = []

For code and runbooks, schedule jobs on repo pushes and wiki updates:

python
# Example for code files
from pathlib import Path

def code_chunks_from_path(path: Path):
    # naive: split by function/class markers; use a real parser in production
    text = path.read_text(errors="ignore")
    chunks = []
    buf = []
    for line in text.splitlines():
        buf.append(line)
        if line.strip().startswith(("def ", "class ")) and len(buf) > 80:
            chunks.append("\n".join(buf))
            buf = [line]
    if buf:
        chunks.append("\n".join(buf))
    for i, chunk in enumerate(chunks):
        yield {
            "text": chunk,
            "meta": {
                "type": "code",
                "path": str(path),
                "chunk": i,
                "repo": "ranking-service",
                "language": path.suffix,
            }
        }

code_docs = []
for p in Path("./repo").rglob("*.py"):
    code_docs.extend(list(code_chunks_from_path(p)))
upsert_points(INDEX_CODE, code_docs)

Retrieval with Hybrid + Rerank

Below is a simple dense retrieval followed by a reranker (swap for Cohere/ColBERT as needed). In production, also query BM25 and merge.

python
from typing import List, Tuple
import numpy as np

# naive reranker using a cross-encoder
from sentence_transformers import CrossEncoder
reranker = CrossEncoder("BAAI/bge-reranker-v2-m3")


def search(index: str, query: str, filters: dict | None = None, top_k: int = 50):
    qvec = embedder.encode([query], normalize_embeddings=True)[0]
    res = client.search(
        collection_name=index,
        query_vector=qvec.tolist(),
        limit=top_k,
        query_filter={"must": [{"key": k, "match": {"value": v}} for k, v in (filters or {}).items()]}
    )
    docs = [(hit.payload, hit.score) for hit in res]
    return docs


def hybrid_merge_and_rerank(query: str, env: str, service: str, k_dense=50, k_final=10):
    candidates = []
    # Dense search per index (add lexical BM25 results in production)
    candidates += [("logs",) + x for x in search(INDEX_LOGS, query, {"env": env, "service": service}, k_dense)]
    candidates += [("code",) + x for x in search(INDEX_CODE, query, {"repo": f"{service}-service"}, k_dense)]
    candidates += [("runbooks",) + x for x in search(INDEX_RUNBOOKS, query, {}, k_dense)]

    texts = [c[1]["text"] if "text" in c[1] else c[1].get("summary", "") for c in [
        (c[0], {"text": c[1].get("text", "")}) for c in candidates
    ]]
    # Build pairs for reranker
    pairs = [[query, t] for t in texts]
    scores = reranker.predict(pairs)
    ranked = sorted(zip(candidates, scores), key=lambda x: x[1], reverse=True)[:k_final]
    return [
        {
            "source": src,
            "payload": payload,
            "vector_score": vscore,
            "rerank_score": rscore,
        }
        for (src, payload, vscore), rscore in ranked
    ]

Answer Generation with Structured Output

Use an LLM that supports function calling or JSON output. Provide only the top reranked chunks with citations.

python
import os
import openai
openai.api_key = os.environ["OPENAI_API_KEY"]

SYSTEM = """
You are an on-call assistant. Read the CONTEXT and produce:
- summary: 1-2 sentences
- likely_cause: one paragraph
- fix_steps: ordered list of steps
- references: list of {source, id/locator, reason}
- confidence: 0-1
Answer ONLY with JSON.
"""


def build_context(snippets):
    ctx = []
    for i, s in enumerate(snippets, 1):
        meta = s["payload"]
        text = meta.get("text", "")
        locator = meta.get("path") or meta.get("trace_id") or meta.get("ts")
        ctx.append(f"[{i}] ({s['source']}) {locator}\n{text}")
    return "\n\n".join(ctx)


def answer(query: str, env: str, service: str):
    snippets = hybrid_merge_and_rerank(query, env, service)
    context = build_context(snippets)
    messages = [
        {"role": "system", "content": SYSTEM},
        {"role": "user", "content": f"GOAL: {query}\nCONTEXT:\n{context}"}
    ]
    resp = openai.ChatCompletion.create(
        model="gpt-4o-mini",  # swap your model of choice
        messages=messages,
        temperature=0.1
    )
    return resp.choices[0].message["content"]

In production, enforce a context size cap, mask secrets, and verify that every assertion in the output is backed by a citation.

Freshness and Reindexing Strategies That Work Under Load

• Streaming logs/traces: micro-batch embeddings every 1–5 seconds; monitor backlog and autoscale embedding workers.
• Git hooks: on push, compute a content hash per chunk; skip unchanged; update “active branch” view for main and release branches.
• Wiki polling or webhooks: fetch delta, re-embed changed sections. Maintain “last-reviewed” metadata and page owners.
• Warm caches after deploy: pro-actively precompute queries like “known issues for service X” and keep top candidates hot.
• Sliding TTL windows: keep dense embeddings for the hot window; index lexically beyond that and rely on reranking only when necessary.

Security and Privacy Deep Dive

• Policy enforcement as code: define who can see what via ABAC (team, project, data classification). Encode as server-side filters injected into every query.
• PII/secrets: use deterministic tokenization (format-preserving) at ingest; store reversible mapping in a KMS-sealed vault; reverse only post-authorization.
• Prompt injection defense:
  • Treat all retrieved text as adversarial.
  • Use a strict system prompt and a fixed response schema.
  • Strip or neutralize strings like “ignore previous instruction” from logs before concatenation.
  • Optionally run a “harmful-instructions” classifier on context and drop offending snippets.
• Redaction-in-context: if a snippet is partially restricted, redact spans and annotate the citation accordingly; never include raw restricted text.

Evaluation: Make It Scientific

Build an eval harness with the following artifacts:

• Questions: natural-language queries derived from real incidents.
• Gold chunks: the minimal set of chunks required to answer correctly.
• Expected JSON answer: summary, cause, and steps.
• Metrics:
  • Retrieval: recall@5/10, precision@10, nDCG@10.
  • Answer: groundedness (LLM-as-judge or rule-based citation checks), exactness of steps, and human-rated usefulness.
  • System: p95 latency, cost per query, and rate of policy violations detected.

Automate:

• Run on every change to embeddings, chunking, or reranker.
• Sample drift: keep a monthly rotating set of fresh incidents to detect regressions.
• Synthetic generation: create synthetic stack traces and runbooks to augment rare failures—label clearly and avoid polluting production indices.

Cost, Latency, and Scale

• Embeddings cost: choose small models for logs to keep throughput high; use larger models for runbooks if quality bumps precision.
• Reranking budget: rerank 100–200 candidates; the marginal gain beyond 200 is usually small; cache reranker scores for recurring queries within an incident.
• Token budget: compress context with extractive summarization; prefer many short chunks with high relevance over long, noisy blocks.
• Caching: cache query -> topK candidates with a short TTL (30–120s) during active incidents; refresh on deploys.
• Hardware: if self-hosting, run HNSW with enough RAM to keep vectors in memory; pin hot shards.

A Reference Prompt Pack

• Root-cause analysis:

text
Task: Identify likely root cause from CONTEXT and list the top 3 supporting evidence snippets. If multiple causes possible, rank by posterior likelihood.
Output JSON: {root_cause, evidence: [{id, quote, reason}], alternatives: [{hypothesis, evidence}], confidence}

• Fix steps with guardrails:

text
Constraints: Do not suggest destructive actions in production (e.g., drop tables, delete indexes). Propose reversible mitigations first. Mark risky steps.
Output JSON: {steps: [{action, rationale, risk: low|med|high, rollback}], dependencies: [services], runbook_link}

• Code pinpointing:

text
Goal: Find the code/config responsible for the error and suggest a minimally invasive patch with tests.
Output JSON: {files: [{path, lines, reason}], patch, tests}

Integrations: PagerDuty, Jira, Slack

• On incident creation, attach the RAG assistant to the incident channel.
• Auto-post “first signal” within 60 seconds: top 3 snippets + a one-line hypothesis with confidence.
• Add buttons: “Open runbook,” “Create Jira fix ticket,” “Roll back last deploy” (guarded by policy).
• Log all queries and responses for later postmortem and to grow the ground-truth dataset.

Pitfalls and Antipatterns

• One big index for everything: hurts recall and governance; use domain-specific indexes.
• Over-reliance on dense search: logs and code need exact matches; never skip BM25.
• Stale indices: if your logs index lags by minutes, your assistant will feel like a toy.
• Unchecked context: letting sensitive snippets leak into the prompt is a policy incident waiting to happen.
• No eval set: you won’t know if changes help or hurt under real pressure.

A 30/60/90-Day Plan

• Days 0–30:
  • Stand up hybrid indexes for logs and runbooks. Ingest last 7 days of logs.
  • Build a minimal reranking pipeline and a JSON answer format.
  • Create a 100-question eval set from recent incidents.
• Days 31–60:
  • Add code/indexing with AST-aware chunking and owner metadata.
  • Implement streaming embeddings for logs/traces with <60s freshness.
  • Add governance: ABAC filters, PII redaction, and prompt firewall.
  • Integrate with incident tooling (PagerDuty/Jira/Slack).
• Days 61–90:
  • Introduce multi-hop retrieval and query rewrite.
  • Add ColBERT or a strong cross-encoder; optimize latency.
  • Expand evals and run A/B during incidents; measure MTTR impact.
  • Backfill postmortems and generate diffs to update runbooks.

Example: Time-Weighted Retrieval Scoring

Apply a smooth time-decay for logs so recent events dominate:

python
import math

def time_decay_score(now_ts, doc_ts, half_life_minutes=60):
    dt = max(0, now_ts - doc_ts) / 60.0  # minutes
    return 0.5 ** (dt / half_life_minutes)

# Combine with ANN score (cosine similarity in [0,1]) and rerank score
final = ann_score * 0.6 + rerank_score_norm * 0.3 + time_decay * 0.1

Tune weights per domain. For runbooks, drop the time factor; for traces, increase time weight during active incidents.

Continuous Improvement Loop

• Every incident generates new knowledge. Turn chat summaries into draft runbook patches.
• Ask owners to approve or edit; auto-index on merge.
• Capture “fix efficacy” (did the steps work?) and feed that back into reranking training signals.
• Detect recurring patterns: if the same cause appears 3+ times, create a “known issue” card with canonical remediation.

References and Further Reading

• BEIR: A Heterogeneous Benchmark for Information Retrieval (Thakur et al.).
• MTEB: Massive Text Embedding Benchmark (Muennighoff et al.).
• ColBERTv2: Effective and Efficient Passage Search via Late Interaction.
• BAAI bge family (bge-small/large, bge-reranker-v2-m3).
• OpenTelemetry specification for traces and metrics.
• Qdrant, Milvus, Weaviate, Pinecone, Vespa, Elastic/OpenSearch kNN docs.

Closing Opinion

There’s no magic in RAG for debugging—only disciplined retrieval engineering applied to operational data. The winning setup is opinionated: domain-specific indexes, hybrid retrieval with strong reranking, aggressive freshness, and non-negotiable governance. Do this, and your LLM stops guessing and starts fixing. The payoff is concrete: lower MTTR, fewer escalations, and a calmer on-call.