IVQA 901-950
901. Measuring Accuracy of Multi-step Reasoning
Use task-specific ground truths or intermediate checkpoints. Compare step-wise answers using logical correctness, not just final result.
902. Benchmarks for Chain-of-Thought (CoT) Quality
Examples:
GSM8K (math)
StrategyQA (reasoning)
HotpotQA (multi-hop) Metrics: step accuracy, logical coherence, answer traceability.
903. Testing Consistency Across Tasks
Repeat same inputs over time or under varied load. Use deterministic decoding or track divergence via embedding distance and token-level diffing.
904.Metrics for Helpfulness Beyond Task Completion
Engagement length, user rating, guidance quality, clarification offered, and number of helpful reformulations or tool suggestions.
905. Identifying Tool/Step Hallucinations
Cross-reference tool calls with allowed list. Log invocation traces. Use rule-based filters or semantic validation of step descriptions.
906. Rationality Scaffolding
Providing structured steps like “plan → reason → act”. It improves coherence, reduces hallucination, and aligns with cognitive tracing.
907. Evaluating Task Decomposition
Check if subtasks are logically sequenced and scoped. Validate decomposition against expert-curated task graphs.
908. Logging Structure for Replay
Use JSON logs with:
step_id,input,output,tool_used,memory_context,timestamp. Enables replay and debugging.
909. A/B Testing Agent Policies
Split traffic or use simulated task sets. Compare completion rate, user ratings, quality scores, and retry count.
910. Evaluating Emergent Behavior
Look for patterns not seen in base training, e.g., agent collaboration, novel task breakdowns. Use clustering and behavior audits.
911. Preserving State in Long Conversations
Store evolving memory: user profile, session context, goals. Use memory compression or summarization for long histories.
912. Detecting Topic Shifts
Use intent classifiers or embedding-based clustering. Topic transition thresholding using cosine similarity on recent messages.
913. Guiding Users with Unclear Intent
Tactics:
Clarifying questions
Offer multiple interpretations
Confirm assumptions: “Did you mean X or Y?”
914. Handling Ambiguous Follow-ups
Techniques:
Slot filling
Context window re-evaluation
Prompt agents to explicitly confirm ambiguity
915. Reset/Pause/Bookmark in UX
Add UI actions for state snapshot. Persist memory with labels (e.g., “bookmark: tax conversation”). Support rollback or branch.
916. Controlling Verbosity Across Turns
Use user preference (concise/detailed), or token budget tuning. Set verbosity flags in memory or model instructions.
917. Dialogue Guards Against Prompt Hijacking
Input sanitization, boundary tokens, prefix constraints. Use LLM classifiers to filter adversarial prompts.
918. Response Chaining for Coherence
Use previous response + summary as part of next prompt input. Ensures continuity and grounding in past context.
919. Testing for Regression Errors
Snapshot prior conversations and rerun across model upgrades. Diff responses and score divergence.
920. Dynamic Temperature/Top-p Control
Use conversation phase (intro = high creativity, summary = low). Adjust via feedback loops (e.g., if user is confused, reduce entropy).
921. Combining Dense and Sparse Retrieval
Hybrid retrievers (e.g., ColBERT, SPLADE) or merge BM25 with vector scores. Rank via linear fusion or learned re-ranker.
922. Designing QA with Search → Rerank → Generate
Pipeline:
Search: retrieve k passages
Rerank: cross-encoder filters top-n
Generate: LLM answers with citations from context.
923. Measuring Latency, Grounding, Recall
Latency: avg search + generation time Grounding: citation match rate Recall: % of gold answer-supporting docs retrieved.
924. Handling Irrelevant Passages
Score passages for relevance. Filter via semantic thresholding or hallucination classifiers before passing to LLM.
925. Intent Detection for Query Reformulation
Classify query type (navigational, informational, transactional). Use LLM to suggest improved formulations or disambiguations.
926. Storing Feedback for Fine-tuning
Store
query → feedback → doc relevancetriplets. Use for retrieval model fine-tuning or prompt tuning (RLHF or DPO).
927. Cross-Encoder Reranking Use Cases
Use when precision is critical (e.g., legal or medical search). Trades off latency for better context accuracy.
928. Semantic Deduplication of Results
Use embedding similarity (cosine) and remove near-duplicates. Can also cluster and show summaries.
929. Architecture for Search + Chat Hybrid
Components:
Retriever + Memory
Reranker
LLM + Tool use
Stateful memory store Enable seamless transition from retrieval to multi-turn.
930. Testing for Hallucinated Citations
Parse generated citations → Match to source
If fabricated or misattributed, flag. Use citation classifiers to score hallucination risk.
931. Evaluating Translation in Low-resource Languages
Use BLEU/ChrF scores + human raters. Validate cultural meaning, not just literal accuracy. Reference FLORES benchmarks.
932. Role of Locale Embeddings
Embed cultural, linguistic, and domain-specific context. Improves retrieval relevance and translation accuracy.
933. Tone Consistency Across Languages
Prompt models with tone tags (e.g.,
formal,playful). Use tone evaluation LLMs post-translation.
934. Preserving Names, Units, Idioms
Use tags like
<NAME>or<IDM>. Apply post-processing or constrain model with glossaries and domain dictionaries.
935. Fine-tuning on Bilingual Support Logs
Align source-target turns. Train on pairs using supervised translation loss. Add correction feedback for robustness.
936. Detecting Cultural Insensitivity
Use moderation LLMs trained on offensive/culturally risky content. Include culture-specific lexicons and flags.
937. Region-Specific Prompt Templates
Create templates with country-specific spellings, greetings, references. Store templates by locale key.
938. Transliteration vs. Translation vs. Localization
Transliteration: Phonetic spelling Translation: Language conversion Localization: Cultural adaptation (e.g., changing “football” to “cricket”).
939. Handling Code-Mixing (e.g., Hinglish)
Train on mixed-language corpora. Use multilingual tokenizers and apply entity-aware prompting.
940. Evaluating Culturally Appropriate Phrasing
Use cultural review agents or annotators. Score based on alignment with local idioms, politeness, and tone expectations.
941. Handling Failed Completions Gracefully
Detect via timeout, empty output, or error codes. Respond with retry or safe fallback message: “Let me recheck that...”
942. Multi-step Workflow Retry Logic
Isolate stages as idempotent. Wrap each in try-catch with backoff and retry counters. Log partial progress.
943. Fallback to Search-Based Answers
If model fails or outputs low-confidence response, reroute query to RAG or static KB. Show source-backed snippets.
944. Monitoring Token Quota Exhaustion
Track per-user/session token usage. Alert or degrade gracefully if nearing quota. Use OpenAI
usageAPI or internal budget manager.
945. Caching Strategies
Cache by prompt hash or retrieval fingerprint. Store both final answers and intermediate chunks (retrieval or summary).
946. Graceful Degradation to Static Responses
Maintain static FAQ/heuristic lookup layer. Route to it during model outage or latency spike.
947. Validating Retry-Worthy Outputs
Use LLM-based output scoring or anomaly detection. Retry if incoherent, out-of-spec, or flagged by rule filters.
948. Cross-provider Redundancy Strategy
Use abstraction layer (e.g., LangChain, LlamaIndex) to switch between OpenAI, Anthropic, Mistral. Fallback if one fails.
949. Guardrails Against Silent Failures
Include heartbeat checks, completion length validation, logging for every call. Monitor for abnormal token patterns.
950. Circuit Breakers vs. Retries vs. Escalation
Circuit Breaker: Stops further calls on repeated failure Retry: Temporary, short-term Human Escalation: Triggered after persistent failure or critical confidence drop.
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