llm-relevance-scoring

name	llm-relevance-scoring
description	Use when assigning quality or relevance scores to individual pieces of content in a text corpus, when distinguishing substantive contributions from low-effort content, when identifying authority peaks where a user served as a primary knowledge source, or when building a scoring pipeline with local or remote LLMs via Ollama or API

LLM-Based Relevance Scoring

Overview

Use a Large Language Model as a judge to assign 0-100 relevance scores to individual content items, evaluating substance over surface-level signals. The core principle: a well-calibrated LLM with an explicit rubric scores content more consistently than ad-hoc heuristics, but scores are measurements -- not ground truth -- and require calibration, spot-checks, and documented rubrics.

When to Use

• Corpus of text items (posts, comments, articles, reviews, messages) needs quality or relevance ranking
• Need to separate substantive contributions from low-effort content (one-liners, memes, "+1" replies)
• Identifying "Authority Peaks" -- content where a user served as a primary knowledge source for others
• Building a scoring pipeline for batch processing with checkpointing
• Need reproducible, documented scoring criteria rather than subjective impressions

When NOT to use:

• Corpus has fewer than 20 items -- manual review is faster and more reliable
• Content is non-textual (images, audio) without transcription
• You need real-time scoring with sub-second latency (use heuristics instead)
• A simple keyword or regex filter would suffice (do not use an LLM for what grep can do)

digraph scoring_decision {
    rankdir=LR;
    "Need to rank\ncontent quality?" [shape=diamond];
    "Corpus > 20 items?" [shape=diamond];
    "Simple pattern\nsufficient?" [shape=diamond];
    "Use this skill" [shape=box];
    "Manual review" [shape=box];
    "Use regex/keyword\nfiltering" [shape=box];

    "Need to rank\ncontent quality?" -> "Corpus > 20 items?" [label="yes"];
    "Corpus > 20 items?" -> "Simple pattern\nsufficient?" [label="yes"];
    "Corpus > 20 items?" -> "Manual review" [label="no"];
    "Simple pattern\nsufficient?" -> "Use regex/keyword\nfiltering" [label="yes"];
    "Simple pattern\nsufficient?" -> "Use this skill" [label="no"];
}

Quick Reference

Concept	Recommended Approach	Anti-pattern
Rubric	Explicit 5-level rubric with examples per level	Vague "rate quality 0-100" prompt
Few-shot examples	5-10 calibration examples spanning the full score range	Zero-shot or examples only at extremes
Temperature	0.1-0.3 for scoring consistency	Default (1.0) introduces variance
Batch size	10-50 items per batch with checkpoint after each	Entire corpus in one pass, no checkpoints
Calibration	Score 20 items manually, compare to LLM, adjust rubric until Cohen's kappa > 0.7	No calibration, trust LLM output directly
Cross-validation	Score a 10% sample with a second model, check correlation	Single model, no validation
Authority detection	Score reply chains for knowledge asymmetry	Count upvotes or length as proxy for authority
Spot-checks	Human-review 5-10% of scored items per batch	Zero human review

Workflow

Copy this checklist and track progress:

LLM Relevance Scoring Progress:
- [ ] Step 1: Define the scoring rubric
- [ ] Step 2: Build few-shot calibration examples
- [ ] Step 3: Configure the LLM scoring environment
- [ ] Step 4: Run calibration pass and validate
- [ ] Step 5: Batch-score the corpus with checkpoints
- [ ] Step 6: Detect Authority Peaks
- [ ] Step 7: Cross-validate with a second model (optional but recommended)
- [ ] Step 8: Human spot-check and adjust
- [ ] Step 9: Write findings to docs/analysis/06-llm-relevance-scoring.md

Step 1: Define the Scoring Rubric

The rubric is the single most important artifact. Without it, scores are meaningless.

Five-level rubric template (adapt to your domain):

Score Range	Level	Criteria	Example Signals
0-19	Noise	No substantive content. Memes, single-word replies, deleted/empty, spam, off-topic.	"+1", "lol", "this", "[deleted]", bot-generated filler
20-39	Low Substance	Minimal original thought. Agreement/disagreement without reasoning, personal anecdotes without generalizable insight.	"I agree", "Same thing happened to me", opinion without evidence
40-59	Moderate	Contains a clear point with some supporting detail. On-topic, contributes to discussion, but not a primary source.	Shares a relevant link, summarizes known information, asks a clarifying question
60-79	High Substance	Original analysis, detailed explanation, or synthesis of multiple sources. Demonstrates expertise or deep familiarity.	Step-by-step troubleshooting, comparative analysis, detailed how-to
80-100	Authority Peak	Primary knowledge source. Original research, first-hand expert testimony, comprehensive synthesis that others reference. Demonstrates command of the subject.	Definitive technical explanation others cite, original data/findings, corrects widespread misconceptions with evidence

Rubric design rules:

• Each level MUST have concrete, observable criteria -- not subjective impressions
• Criteria must be domain-adapted (technical content differs from creative writing)
• The gap between adjacent levels must be clear enough that two humans would agree 70%+ of the time
• Document the rubric in the output report -- scores without a rubric are uninterpretable

Step 2: Build Few-Shot Calibration Examples

Select 5-10 real items from the corpus that span the full score range. Score them manually, then include them in every scoring prompt as calibration anchors.

CALIBRATION_EXAMPLES = [
    {
        "content": "lol",
        "score": 5,
        "reasoning": "Single-word reaction with no substantive content."
    },
    {
        "content": "I had the same problem last week but can't remember how I fixed it.",
        "score": 25,
        "reasoning": "Personal anecdote acknowledging the topic but providing no actionable information."
    },
    {
        "content": "You need to update your GPU drivers. Go to nvidia.com/drivers and select your card model.",
        "score": 55,
        "reasoning": "Correct, actionable advice but brief. Points in the right direction without detailed explanation."
    },
    {
        "content": "This is a known issue with kernel 6.1+ when using NVIDIA 535 drivers. The fix is to add 'nvidia-drm.modeset=1' to your GRUB config. Here's why: the DRM subsystem changed its initialization order in 6.1, and without explicit modesetting, the framebuffer handoff fails during resume. I verified this on three machines running Fedora 39.",
        "score": 85,
        "reasoning": "Expert-level response with root cause analysis, specific fix, explanation of the underlying mechanism, and empirical verification. This is a primary knowledge source."
    },
]

Selection criteria for calibration examples:

• At least one example per rubric level
• Choose examples where the score is unambiguous (boundary cases confuse calibration)
• Prefer examples from the actual corpus over synthetic ones
• Include one example that looks long but is low-substance (tests length-bias resistance)

Step 3: Configure the LLM Scoring Environment

Local via Ollama (recommended for privacy and cost):

import requests
import json
import time

OLLAMA_BASE = "http://localhost:11434"

def score_with_ollama(content, rubric, examples, model="llama3.1:8b"):
    """Score a single content item using a local Ollama model."""
    prompt = build_scoring_prompt(content, rubric, examples)

    response = requests.post(
        f"{OLLAMA_BASE}/api/generate",
        json={
            "model": model,
            "prompt": prompt,
            "stream": False,
            "options": {
                "temperature": 0.2,  # Low temperature for consistency
                "num_predict": 256,  # Cap output length
            }
        },
        timeout=120
    )
    response.raise_for_status()
    return parse_score_response(response.json()["response"])


def build_scoring_prompt(content, rubric, examples):
    """Build the scoring prompt with rubric and few-shot examples."""
    example_block = "\n\n".join(
        f"Content: \"{ex['content']}\"\nScore: {ex['score']}\nReasoning: {ex['reasoning']}"
        for ex in examples
    )

    return f"""You are a content relevance scorer. Score the following content on a 0-100 scale using this rubric:

{rubric}

Here are calibration examples:



Now score this content. Respond with ONLY a JSON object containing "score" (integer 0-100) and "reasoning" (one sentence).

Content: \"\"
"""


 ():
    
    
    :
        
        start = raw_response.index()
        end = raw_response.rindex() + 
        parsed = json.loads(raw_response[start:end])
        score = (parsed[])
        reasoning = parsed.get(, )
          <= score <= :
             {: score, : reasoning}
     (ValueError, KeyError, json.JSONDecodeError):
        

    
     re
     = re.search(, raw_response)
     :
        score = (.group())
          <= score <= :
             {: score, : raw_response.strip()}

     {: -, : }

Model selection guidance:

Model	Strengths	Limitations
Llama 3.1 8B (Ollama)	Fast, runs on 8GB VRAM, good for bulk scoring	May struggle with nuanced authority detection
Llama 3.1 70B (Ollama)	Strong reasoning, better calibration	Requires 40GB+ VRAM or quantized
Mistral 7B (Ollama)	Fast, good instruction following	Less consistent on subjective rubrics
GPT-4 / GPT-4o (API)	Best calibration, highest human agreement	Cost, rate limits, data leaves your machine
Claude (API)	Strong reasoning, good rubric adherence	Cost, rate limits, data leaves your machine

Fallback chain: If the primary model is unavailable, fall back gracefully:

1. Primary: local Ollama model
2. Fallback: smaller local model (e.g., 8B instead of 70B)
3. Last resort: API-based model with rate limiting
4. If no LLM available: log items as "unscored" and continue pipeline

Step 4: Run Calibration Pass and Validate

Before scoring the full corpus, calibrate on a small set.

def calibration_check(human_scores, llm_scores):
    """Compare human and LLM scores. Target: Cohen's kappa > 0.7 on binned scores."""
    # Bin into 5 levels for kappa calculation
    def to_bin(score):
        if score < 20: return 0
        if score < 40: return 1
        if score < 60: return 2
        if score < 80: return 3
        return 4

    human_bins = [to_bin(s) for s in human_scores]
    llm_bins = [to_bin(s) for s in llm_scores]

    # Cohen's kappa (manual calculation, no sklearn dependency)
    from collections import Counter
    n = len(human_bins)
    observed_agree = sum(1 for h, l in zip(human_bins, llm_bins) if h == l) / n
    h_dist = Counter(human_bins)
    l_dist = Counter(llm_bins)
    expected_agree = sum(
        (h_dist.get(k, 0) / n) * (l_dist.get(k, 0) / n) for k in range(5)
    )
    kappa = (observed_agree - expected_agree) / ( - expected_agree)  expected_agree <   

    
    mae = ((h - l)  h, l  (human_scores, llm_scores)) / n

     {
        : (kappa, ),
        : (mae, ),
        : (observed_agree, ),
        : kappa >=   mae <= 
    }

Calibration procedure:

1. Manually score 20 items spanning all rubric levels
2. Score the same 20 items with the LLM
3. Compute Cohen's kappa (binned into 5 levels) and mean absolute error
4. If kappa < 0.7 or MAE > 15: revise rubric wording, add/adjust examples, re-score
5. Repeat until calibration passes
6. Document final calibration metrics in the report

If calibration never passes: The rubric may be too subjective for the chosen model. Options:

• Simplify to 3 levels (low/medium/high) instead of 5
• Use a more capable model
• Accept lower calibration and flag all scores as "approximate"

Step 5: Batch-Score the Corpus with Checkpoints

REQUIRED SUB-SKILL: Use automated-orchestration for checkpoint/resume patterns.

import json, time
from pathlib import Path

def batch_score(items, model, rubric, examples, checkpoint_path="checkpoints/scoring.json",
                batch_size=25, rate_limit_delay=0.5):
    """Score items in batches with checkpoint/resume."""
    checkpoint_path = Path(checkpoint_path)
    checkpoint_path.parent.mkdir(parents=True, exist_ok=True)

    # Load existing checkpoint
    if checkpoint_path.exists():
        state = json.loads(checkpoint_path.read_text())
    else:
        state = {"scored": {}, "errors": [], "started_at": time.time()}

    already_scored = set(state["scored"].keys())
    remaining = [(i, item) for i, item in enumerate(items)
                 if str(i) not in already_scored]

    print(f"Scoring {len(remaining)} items ({len(already_scored)} already done)")

    for batch_start in range(0, len(remaining), batch_size):
        batch = remaining[batch_start:batch_start + batch_size]

        for idx, item in batch:
            try:
                result = score_with_ollama(
                    item["content"], rubric, examples, model=model
                )
                state[][(idx)] = {
                    : result[],
                    : result[],
                    : item.get(, idx),
                }
                time.sleep(rate_limit_delay)
             Exception  e:
                state[].append({
                    : idx,
                    : (e),
                    : time.time()
                })

        
        tmp = checkpoint_path.with_suffix()
        tmp.write_text(json.dumps(state, indent=, default=))
        tmp.rename(checkpoint_path)
        (
              )

    state[] = time.time()
    checkpoint_path.write_text(json.dumps(state, indent=, default=))
     state

Step 6: Detect Authority Peaks

An Authority Peak is content where a user served as a primary knowledge source -- not merely participating, but providing information others could not easily obtain elsewhere.

def detect_authority_peaks(scored_items, threshold=75):
    """Identify content items that qualify as Authority Peaks.

    Authority Peak criteria (ALL must be met):
    1. Relevance score >= threshold (default 75)
    2. Contains original information (not just linking/quoting)
    3. Demonstrates domain expertise or first-hand knowledge
    """
    peaks = []
    for item in scored_items:
        if item["score"] < threshold:
            continue

        # Authority signals (check reasoning field for these markers)
        reasoning = item.get("reasoning", "").lower()
        authority_signals = [
            "expert", "first-hand", "original", "primary source",
            "detailed explanation", "comprehensive", "corrects",
            "demonstrates expertise", "authority"
        ]
        signal_count = sum(1 for s in authority_signals if s in reasoning)

        peaks.append({
            **item,
            "authority_signal_count": signal_count,
            "is_authority_peak": signal_count >= 1 or item["score"] >= 85
        })

    return [p for p in peaks if p["is_authority_peak"]]

Authority Peak characteristics:

• High relevance score alone is necessary but NOT sufficient
• Look for knowledge asymmetry: the author knows something the audience does not
• Original analysis, first-hand experience, or synthesis of primary sources
• Other users reference, build on, or defer to this content
• Length is NOT a reliable signal -- a 2-sentence expert correction can be an Authority Peak

Step 7: Cross-Validate with a Second Model

Score a random 10% sample with a different model. High correlation (Pearson r > 0.75) between models increases confidence. Low correlation flags rubric ambiguity.

import random

def cross_validate(items, primary_scores, secondary_model, rubric, examples,
                   sample_fraction=0.10):
    """Score a sample with a second model and compare."""
    sample_size = max(10, int(len(items) * sample_fraction))
    sample_indices = random.sample(range(len(items)), sample_size)

    secondary_scores = {}
    for idx in sample_indices:
        result = score_with_ollama(
            items[idx]["content"], rubric, examples, model=secondary_model
        )
        secondary_scores[idx] = result["score"]

    # Pearson correlation
    primary_sample = [primary_scores[idx] for idx in sample_indices]
    secondary_sample = [secondary_scores[idx] for idx in sample_indices]

    n = len(primary_sample)
    mean_p = sum(primary_sample) / n
    mean_s = sum(secondary_sample) / n
    cov = sum((p - mean_p) * (s - mean_s) for p, s in zip(primary_sample, secondary_sample)) / n
    std_p = (sum((p - mean_p) ** 2 for p in primary_sample) / n) ** 0.5
    std_s = (sum((s - mean_s) ** 2 for s in secondary_sample) / n) ** 0.5

    pearson_r = cov / (std_p * std_s) if std_p > 0 and std_s > 0  

     {
        : n,
        : (pearson_r, ),
        : (mean_p, ),
        : (mean_s, ),
        : pearson_r >= 
    }

Step 8: Human Spot-Check and Adjust

After batch scoring, randomly select 5-10% of items for human review. Focus on:

1. Boundary cases (scores near 40, 60, 80 -- the rubric level transitions)
2. Extreme scores (0-10 and 90-100 -- verify these are genuinely extreme)
3. Parse failures (any items with score = -1)
4. Score clusters -- if >50% of items score within a 10-point range, the rubric may lack discrimination

Adjustment protocol:

• If human disagrees with >20% of spot-checked scores: revise rubric, re-calibrate, re-score
• If disagreements cluster at specific rubric levels: refine that level's criteria
• If scores show systematic bias (LLM scores 15+ points higher/lower than human): add a bias correction offset and document it

Step 9: Write Report

Write all findings to docs/analysis/06-llm-relevance-scoring.md.

Report Output Template

The final report MUST be written to docs/analysis/06-llm-relevance-scoring.md with this structure:

# LLM-Based Relevance Scoring Analysis

## Methodology
- Corpus: [N items, source description, average content length]
- Model(s) used: [primary model, secondary model if cross-validated]
- Rubric: [5-level rubric with criteria -- include full rubric or reference]
- Calibration: [kappa score, MAE, number of calibration items]
- Temperature: [value used]
- Few-shot examples: [count, selection criteria]

## Calibration Results
| Metric | Value | Threshold | Pass? |
|--------|-------|-----------|-------|
| Cohen's kappa (5-bin) | [value] | >= 0.7 | [yes/no] |
| Mean Absolute Error | [value] | <= 15 | [yes/no] |
| Observed agreement | [value] | -- | -- |

## Score Distribution
| Score Range | Level | Count | Percentage |
|-------------|-------|-------|------------|
| 0-19 | Noise | [N] | [%] |
| 20-39 | Low Substance | [N] | [%] |
| 40-59 | Moderate | [N] | [%] |
| 60-79 | High Substance | [N] | [%] |
| 80-100 | Authority Peak | [N] | [%] |

**Mean score:** [value]
**Median score:** [value]
**Standard deviation:** [value]

## Authority Peaks
[List of content items scoring 80+ with authority signals]

| Item ID | Score | Authority Signals | Summary |
|---------|-------|-------------------|---------|
| ... | ... | ... | ... |

## Cross-Validation (if performed)
| Metric | Value |
|--------|-------|
| Sample size | [N] |
| Pearson r | [value] |
| Mean score (primary) | [value] |
| Mean score (secondary) | [value] |
| Correlation assessment | [strong/moderate/weak] |

## Human Spot-Check Results
- Items reviewed: [N] ([%] of corpus)
- Agreement rate: [%]
- Bias direction: [LLM scores higher/lower/neutral]
- Adjustments made: [description or "none"]

## Limitations and Caveats
- [LLM scores are model-dependent, not objective truth]
- [Rubric was designed for this specific corpus/domain]
- [Calibration metrics and any shortfalls]
- [Items that could not be scored and why]

Good Patterns

• Explicit rubric with observable criteria -- every level defined by what you can SEE in the content, not how it "feels"
• Few-shot calibration examples spanning the full range -- anchors the model's scoring distribution
• Low temperature (0.1-0.3) -- reduces score variance between identical re-runs
• Checkpoint after every batch -- avoids re-scoring hundreds of items after a crash
• Cross-validation with a second model -- agreement between models is stronger evidence than one model's output
• Human spot-checks on boundary and extreme scores -- catches systematic miscalibration
• Document the rubric in the report -- scores are uninterpretable without the rubric that produced them
• Chain-of-thought reasoning -- requiring the LLM to explain its score before outputting the number improves consistency

Anti-Patterns

Anti-Pattern	Why It Fails	Instead
"Rate this content 0-100" with no rubric	LLM has no basis for consistent scoring; scores drift across batches	Provide explicit 5-level rubric with examples
No calibration before bulk scoring	Systematic errors multiply across entire corpus	Always calibrate on 20 items first, target kappa > 0.7
Temperature = 1.0 for scoring	High variance; same content gets different scores on re-runs	Use temperature 0.1-0.3 for scoring tasks
Scoring without few-shot examples	Model invents its own scale; 50 might mean "average" or "mediocre"	Include 5-10 calibration examples in every prompt
Trusting LLM scores as ground truth	LLMs are judges, not oracles; they have systematic biases	Treat scores as one input, not definitive ranking
Processing entire corpus without checkpoints	One failure or timeout loses all progress	Checkpoint every 10-50 items
Using content length as a quality proxy	Long content can be low-quality; short content can be authoritative	Score on substance, explicitly tell LLM to ignore length
Scoring with no rate limiting	Overwhelms local model or exceeds API quotas	Add 0.5-2s delay between items
Skipping human spot-checks	No way to detect systematic bias or rubric failure	Human-review 5-10% of scored items

Boundaries

This skill SHOULD:

• Score content substance using a documented rubric
• Identify high-value contributions (Authority Peaks)
• Produce calibrated, spot-checked scores with documented confidence
• Write a complete report to docs/analysis/06-llm-relevance-scoring.md
• Use checkpointed batch processing for large corpora
• Document the rubric, calibration metrics, and any adjustments

This skill should NOT:

• Treat LLM scores as objective truth -- they are model-dependent measurements
• Score content without human spot-checks on a sample
• Use scores to rank PEOPLE rather than CONTENT -- scores describe artifacts, not authors
• Skip calibration -- uncalibrated scores are meaningless
• Assume authority from score alone -- Authority Peaks require both high scores AND knowledge asymmetry signals
• Process content without rate limiting

Insufficient Data Handling

digraph insufficient_data {
    rankdir=TB;
    node [shape=diamond, style=filled, fillcolor="#f0f0f0"];

    start [label="Corpus size?", shape=diamond];
    tiny [label="< 20 items", shape=box, fillcolor="#ffcccc"];
    small [label="20-50 items", shape=box, fillcolor="#fff3cc"];
    adequate [label="> 50 items", shape=box, fillcolor="#ccffcc"];

    start -> tiny [label="< 20"];
    start -> small [label="20-50"];
    start -> adequate [label="> 50"];

    tiny_action [label="STOP: Manual review instead.\nLLM scoring overhead exceeds benefit.\nList items with manual quality notes.", shape=box, fillcolor="#ffcccc"];
    small_action [label="PROCEED WITH CAVEATS.\nCalibrate on 10 items (50%).\nFlag all scores as exploratory.\nSkip cross-validation (insufficient sample).", shape=box, fillcolor="#fff3cc"];
    adequate_action [label="PROCEED.\nFull pipeline with calibration,\ncross-validation, and spot-checks.", shape=box, fillcolor="#ccffcc"];

    tiny -> tiny_action;
    small -> small_action;
    adequate -> adequate_action;
}

Condition	Action
Corpus < 20 items	Do NOT use LLM scoring. Manual review is faster and more reliable at this scale. List items with brief quality notes instead.
Corpus 20-50 items	Score all items but flag results as "exploratory." Calibrate on at least 10 items. Skip cross-validation (sample too small).
Corpus > 50 items	Full pipeline: calibrate, batch-score, cross-validate, spot-check.
Content too short to score (< 5 words)	Assign score 0-10 automatically with reasoning "Content too brief for meaningful evaluation." Do not send to LLM.
LLM unavailable (Ollama down, API unreachable)	Log items as "unscored" with timestamp. Do NOT skip items silently. Resume when LLM becomes available using checkpoint.
Scores cluster with no variance (>60% within 10-point range)	The rubric lacks discrimination for this corpus. Options: (1) add domain-specific criteria, (2) widen level definitions, (3) switch to 3-level (low/medium/high) scoring.
Parse failures > 10%	Model is not following the output format. Fix: (1) simplify prompt, (2) add stricter output format instructions, (3) try a different model.
Local LLM resources limited (slow GPU, CPU-only)	Use quantized models (Q4_K_M), reduce batch sizes, increase rate_limit_delay, or score a representative sample and extrapolate.
Calibration never passes	Simplify to 3 levels (low/medium/high). If still failing, the task may be too subjective for automated scoring -- report this finding.

Common Mistakes

Mistake	Fix
Scoring without defining the rubric first	Write the rubric BEFORE touching the LLM. The rubric is the specification.
Calibrating on boundary cases	Calibration examples should be UNAMBIGUOUS -- save boundary cases for spot-checks.
Using the same examples for calibration and evaluation	Calibration examples go in the prompt. Evaluation items are separate. Never evaluate an item that appears in the few-shot examples.
Reporting mean score without distribution	Always report the full distribution (5-bin histogram). Mean score alone hides bimodal or skewed distributions.
Treating Authority Peaks as "best content"	Authority Peak means the user was a primary knowledge source. High-quality creative writing or humor may score high but is not an Authority Peak.
Re-scoring the entire corpus after rubric adjustment	Only re-score the calibration set and items near the adjusted boundary. Use checkpoints to preserve stable scores.
Ignoring parse failures	Parse failures (score = -1) indicate prompt or model problems. Fix the root cause; do not average them away.
Not documenting model version	Scores are model-dependent. "llama3.1:8b-q4_K_M" and "llama3.1:8b" may produce different results. Record the exact model string.

Red Flags -- Stop and Reassess

• All scores cluster within a 15-point range -- rubric has no discrimination power
• Calibration kappa < 0.5 after two rubric revisions -- task may not be scorable by this model
• Parse failure rate > 20% -- model cannot follow the output format
• LLM scores correlate more with content length than with substance -- add explicit "ignore length" instruction and re-calibrate
• Authority Peak detection finds > 30% of items -- threshold is too low, or rubric inflates scores
• Human spot-check disagrees with > 30% of scores -- fundamental calibration problem

References

• LLM-as-a-Judge: A Practical Guide (Towards Data Science)
• Calibrating Scores of LLM-as-a-Judge (GoDaddy Engineering)
• LLM Rubric Evaluation (Promptfoo)
• LLM-as-a-Judge: Complete Guide (Evidently AI)
• Using LLMs for Evaluation (Cameron Wolfe)
• Ollama Batch Evaluation Guide (KAUST HPC)
• LLM Evaluation Metrics (Confident AI)
• Ollama GitHub Repository